Text processing network, neural network training method and related equipment

文档序号：1087366 发布日期：2020-10-20 浏览：14次中文

阅读说明：本技术 一种文本处理网络、神经网络训练的方法以及相关设备 (Text processing network, neural network training method and related equipment ) 是由张帅张鹏马鑫典魏俊秋于 2020-05-29 设计创作，主要内容包括：本申请涉及人工智能领域中的序列数据的处理技术,公开了一种文本处理网络、神经网络训练的方法以及相关设备。文本处理网络包括相似度计算模块和生成模块；相似度计算模块,用于接收输入的至少两个第一字符,对至少两个第一字符进行特征提取,得到与至少两个第一字符对应的第一特征信息,并根据第一特征信息计算至少两个第一字符在维度级的第一相似度信息,第一特征信息包括第一字符在至少一个维度的特征信息；生成模块,用于根据第一相似度信息,生成与至少两个第一字符对应的第二特征信息。能够一次性处理两个或两个以上的字符,提供了一种更高效的文本处理网络；第二特征信息中融合了维度级的信息,提高了整个文本处理网络的精度。(The application relates to a processing technology of sequence data in the field of artificial intelligence, and discloses a text processing network, a neural network training method and related equipment. The text processing network comprises a similarity calculation module and a generation module; the similarity calculation module is used for receiving at least two input first characters, extracting the features of the at least two first characters to obtain first feature information corresponding to the at least two first characters, and calculating first similarity information of the at least two first characters in a dimension level according to the first feature information, wherein the first feature information comprises feature information of the first characters in at least one dimension; and the generating module is used for generating second characteristic information corresponding to the at least two first characters according to the first similarity information. The method can process two or more characters at one time, and provides a more efficient text processing network; the second characteristic information fuses the dimension level information, and the precision of the whole text processing network is improved.)

1. A text processing network comprising a feature extraction network, said feature extraction network comprising a similarity calculation module and a generation module;

the similarity calculation module is used for receiving at least two input first characters and performing feature extraction on the at least two first characters to obtain first feature information corresponding to the at least two first characters, wherein the first feature information comprises feature information of the first characters in at least one dimension;

the similarity calculation module is further configured to calculate first similarity information of the at least two first characters at a dimension level according to the first feature information;

the generating module is configured to generate second feature information corresponding to the at least two first characters according to the first similarity information.

2. The network of claim 1, wherein the first feature information comprises a first representation and a second representation, the first representation and the second representation are both matrices, a column of data in the first representation comprises feature information of the at least two first characters in one dimension, and a column of data in the second representation comprises feature information of the at least two first characters in one dimension;

the similarity calculation module is specifically configured to calculate a similarity between the column data in the first representation and the column data in the second representation to obtain the first similarity information.

3. The network of claim 2,

the similarity calculation module is specifically configured to perform transposition processing on the first representation, and calculate a similarity between row data in the transposed first representation and column data in the second representation, so as to obtain the first similarity information.

4. The network according to claim 2, wherein the similarity calculation module is specifically configured to:

performing dot product on the column data in the first representation and the column data in the second representation to generate the similarity; alternatively, the first and second electrodes may be,

calculating a Euclidean distance between column data in the first representation and column data in the second representation to generate the similarity; alternatively, the first and second electrodes may be,

calculating a Manhattan distance between column data in the first representation and column data in the second representation to generate the similarity; alternatively, the first and second electrodes may be,

calculating a mahalanobis distance between the column data in the first representation and the column data in the second representation to generate the similarity; alternatively, the first and second electrodes may be,

calculating a cosine similarity between the column data in the first representation and the column data in the second representation to generate the similarity.

5. The network of claim 2,

the similarity calculation module is specifically configured to perform linear transformation on the original representations corresponding to the at least two first characters to obtain the first representation and the second representation.

6. The network of claim 5,

the similarity calculation module is specifically configured to multiply the original representations corresponding to the at least two first characters by a first linear transformation matrix to obtain the first representation, and multiply the original representations corresponding to the at least two first characters by a second linear transformation matrix to obtain the second representation.

7. The network of claim 2,

the similarity calculation module is specifically configured to perform convolution processing on the original representations corresponding to the at least two first characters to obtain the first representation and the second representation.

8. The network according to any one of claims 1 to 7, wherein the first feature information comprises a third representation comprising feature information of the at least two first characters;

the generating module is specifically configured to perform fusion processing on the third representation and the first similarity information to generate the second feature information.

9. The network according to claim 8, wherein the number of the first characters is N, where N is an integer greater than or equal to 2, and the generating module is specifically configured to:

generating a third-order tensor representation according to the third representation and the first similarity information, wherein the third-order tensor representation comprises N matrixes, each matrix corresponds to one first character, and the feature information of one first character and the similarity information of the first character in the dimension level are fused in one matrix;

and compressing the third-order tensor expression to obtain the second characteristic information.

10. The network of claim 9, wherein the first similarity information and the third representation are matrices;

the generating module is specifically configured to perform tensor product operation on the column data in the first similarity information and the column data of the third representation to obtain the third-order tensor representation; alternatively, the first and second electrodes may be,

the generating module is specifically configured to perform addition operation on the column data in the first similarity information and the column data represented by the third expression to obtain the third-order tensor expression.

11. The network of claim 9, wherein the third order tensor representation includes N matrices, each matrix being a two-dimensional d x d matrix;

the generating module is specifically configured to perform compression processing on the third-order tensor representation along one d direction of the third-order tensor representation to obtain the second feature information, where the second feature information is an N × d two-dimensional matrix, and a compression processing mode includes any one of: convolution, addition, averaging, taking the maximum or taking the minimum.

12. The network of claim 8,

the generating module is specifically configured to perform transposition processing on the third representation, and multiply the transposed third representation by the first similarity information to obtain the second feature information.

13. The network according to any one of claims 1 to 7, wherein the text processing network further comprises a feature processing network, and the feature processing network is configured to perform a classification operation based on the second feature information and output indication information of prediction categories corresponding to the at least two first characters, wherein the classification of the categories is based on semantics of the characters or parts of speech of the characters.

14. The network according to any one of claims 1 to 7, wherein the at least two first characters include a character to be predicted, and the text processing network further includes a feature processing network configured to output a prediction result corresponding to the character to be predicted based on the second feature information, the prediction result indicating a predicted character corresponding to the character to be predicted.

15. The network according to any one of claims 1 to 7, wherein the text processing network further comprises a feature processing network, and the feature processing network is configured to perform a translation operation on the first character based on the second feature information to obtain a translated first character, where the translated first character is in a different language from the first character.

16. A method of training a neural network, the method comprising:

acquiring a first training text, wherein the first training text comprises at least two second characters;

inputting the first training text into a first feature extraction network, and performing feature extraction on the first training text through the first feature extraction network to obtain first feature information corresponding to the at least two second characters, wherein the first feature information comprises feature information of the second characters in at least one dimension;

calculating second similarity information of the at least two second characters at a dimension level according to first feature information corresponding to the at least two second characters through the first feature extraction network;

generating second feature information corresponding to the at least two second characters according to the second similarity information through the first feature extraction network;

outputting a generation processing result corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters through a first feature processing network, wherein the first feature processing network and the first feature extraction network belong to a first text processing network;

and performing iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result and the loss function until a preset condition is met.

17. The training method according to claim 16, wherein the at least two second characters include at least one character to be predicted, and the outputting, by the first feature processing network, the generated processing result corresponding to the at least two second characters based on the second feature information corresponding to the at least two second characters includes:

outputting, by the first feature processing network, a prediction result corresponding to the character to be predicted based on second feature information corresponding to the at least two second characters, the prediction result indicating a predicted character corresponding to the character to be predicted;

the iterative training of the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result and the loss function until a preset condition is met includes:

and performing iterative training on the first text processing network according to the correct character, the predicted character and the loss function corresponding to the character to be predicted until a preset condition is met, and outputting a second feature extraction network, wherein the second feature extraction network is the trained first feature extraction network.

18. Training method according to claim 17, characterized in that the method further comprises:

acquiring a second training text, wherein the second training text comprises at least two third characters;

inputting a second training text into the second feature extraction network to generate second feature information corresponding to the at least two third characters through the second feature extraction network;

performing a classification operation based on second feature information corresponding to the at least two third characters through a second feature processing network, and outputting indication information of prediction categories corresponding to the at least two third characters, wherein the second feature extraction network and the second feature processing network belong to a second text processing network;

and performing iterative training on the second text processing network according to the correct type corresponding to the second training text, the indication information of the prediction type and the loss function until a preset condition is met, and outputting the trained second text processing network.

19. The training method according to claim 16, wherein the at least two second characters include at least one character to be predicted, and the outputting, by the first feature processing network, the generated processing result corresponding to the at least two second characters based on the second feature information corresponding to the at least two second characters includes:

outputting a prediction result corresponding to the character to be predicted based on second feature information corresponding to the at least two second characters and an autoregressive algorithm through the first feature processing network, wherein the prediction result indicates a predicted character corresponding to the character to be predicted;

20. The training method according to claim 16, wherein outputting, by the first feature processing network, the generation processing result corresponding to the at least two second characters based on the second feature information corresponding to the at least two second characters includes:

according to a correct translation text corresponding to the first training text and second feature information corresponding to the at least two second characters, performing translation operation on the first training text through the first feature processing network, and outputting translated second characters, wherein the translated second characters are in different languages from the second characters;

and performing iterative training on the first text processing network according to the correctly translated text, the translated second character and the loss function until a preset condition is met, and outputting the trained first text processing network.

21. A neural network for processing sequence data, the neural network comprising a feature extraction network, the feature extraction network comprising a similarity calculation module and a generation module;

the similarity calculation module is used for receiving at least two input sequence data and performing feature extraction on the at least two sequence data to obtain third feature information corresponding to the at least two sequence data, wherein the third feature information comprises feature information of the at least two sequence data in at least one dimension;

the similarity calculation module is further configured to calculate third similarity information of the at least two sequence data at a dimension level according to the third feature information;

and the generating module is used for generating fourth characteristic information corresponding to the at least two sequence data according to the third similarity information.

22. The network of claim 21, wherein the sequence data is used to indicate any of the following: character information, consumption information, location information, and genetic information.

23. The network according to claim 21 or 22, wherein the third characteristic information comprises a fourth representation and a fifth representation, the fourth representation and the fifth representation are both matrices, a column of data in the fourth representation comprises characteristic information of the at least two sequence data in one dimension, and a column of data in the fifth representation comprises characteristic information of the at least two sequence data in one dimension;

the similarity calculation module is specifically configured to calculate a similarity between the column data in the fourth representation and the column data in the fifth representation to obtain the third similarity information.

24. The network according to claim 21 or 22, wherein the first characteristic information comprises a sixth representation, and the sixth representation comprises characteristic information of the at least two sequence data;

the generating module is specifically configured to perform fusion processing on the sixth representation and the third similarity information to generate the fourth feature information.

25. The network according to claim 21 or 22, wherein the neural network further comprises a feature processing network, and the feature processing network is configured to perform feature processing based on the fourth feature information and output a generation processing result corresponding to the at least two sequence data, wherein the feature processing is any one of the following operations: classification operations, prediction operations, and translation operations.

26. A method of text processing, the method comprising:

inputting at least two first characters into a text processing network, wherein the text processing network comprises a feature extraction network, and the feature extraction network comprises a similarity calculation module and a generation module;

performing feature extraction on at least two first characters through the similarity calculation module to obtain first feature information corresponding to the at least two first characters, wherein the first feature information comprises feature information of the first characters in at least one dimension;

calculating first similarity information of the at least two first characters at a dimension level according to the first characteristic information through the similarity calculation module;

and generating second characteristic information corresponding to the at least two first characters according to the first similarity information through the generating module.

27. The method of claim 26, wherein the first feature information comprises a first representation and a second representation, the first representation and the second representation are both matrices, a column of data in the first representation comprises feature information of the at least two first characters in one dimension, and a column of data in the second representation comprises feature information of the at least two first characters in one dimension;

the calculating, by the similarity calculation module, first similarity information of the at least two first characters at a dimension level according to the first feature information includes:

calculating, by the similarity calculation module, a similarity between the column data in the first representation and the column data in the second representation to obtain the first similarity information.

28. The method according to claim 27, wherein the calculating, by the similarity calculation module, first similarity information of the at least two first characters at a dimension level according to the first feature information comprises:

and performing transposition processing on the first representation through the similarity calculation module, and calculating the similarity between the row data in the transposed first representation and the column data in the second representation to obtain the first similarity information.

29. The method of claim 27, wherein calculating the similarity between the column data in the first representation and the column data in the second representation comprises:

calculating a cosine similarity between the column data in the first representation and the column data in the second representation to generate the similarity.

30. The method of claim 27, wherein the extracting features of the at least two first characters by the similarity calculation module to obtain first feature information corresponding to the at least two first characters comprises:

and performing linear transformation on the original representations corresponding to the at least two first characters through the similarity calculation module to obtain the first representation and the second representation.

31. The method of claim 30, wherein linearly transforming the original representations corresponding to the at least two first characters to obtain the first representation and the second representation comprises:

multiplying the original representations of the at least two first characters by a first linear transformation matrix to obtain the first representation, and multiplying the original representations of the at least two first characters by a second linear transformation matrix to obtain the second representation.

32. The method of claim 27, wherein the extracting features of the at least two first characters by the similarity calculation module to obtain first feature information corresponding to the at least two first characters comprises:

and performing convolution processing on the original representations corresponding to the at least two first characters through the similarity calculation module to obtain the first representation and the second representation.

33. The method according to any one of claims 26 to 32, wherein the first feature information includes a third representation, the third representation including feature information of the at least two first characters;

generating, by the generation module and according to the first similarity information, second feature information corresponding to the at least two first characters, including:

and performing fusion processing on the third representation and the first similarity information through the generating module to generate the second feature information.

34. The method according to claim 33, wherein the number of the first characters is N, where N is an integer greater than or equal to 2, and the fusing the third representation and the first similarity information to generate the second feature information includes:

and compressing the third-order tensor expression to obtain the second characteristic information.

35. The method of claim 34, wherein the first similarity information and the third representation are matrices, and wherein generating a third order tensor representation from the third representation and the first similarity information comprises:

36. The method of claim 34, wherein the third order tensor representation includes N matrices, each matrix being a two-dimensional d x d matrix;

the compressing the third-order tensor expression to obtain the second characteristic information includes:

compressing the third-order tensor representation along a d direction of the third-order tensor representation to obtain the second feature information, wherein the second feature information is an N × d two-dimensional matrix, and a compression processing mode includes any one of the following modes: convolution, addition, averaging, taking the maximum or taking the minimum.

37. The method according to claim 33, wherein the fusing the third representation and the first similarity information to generate the second feature information comprises:

and performing transposition processing on the third representation, and multiplying the transposed third representation by the first similarity information to obtain the second feature information.

38. The method of any of claims 26 to 32, wherein the text processing network further comprises a feature processing network, the method further comprising:

and executing classification operation based on the second characteristic information through the characteristic processing network, and outputting indication information of prediction categories corresponding to the at least two first characters, wherein the classification basis of the categories is the semantics of the characters, or the classification basis of the categories is the parts of speech of the characters.

39. The method of any of claims 26 to 32, wherein the at least two first characters include a character to be predicted, wherein the text processing network further includes a feature processing network, and wherein the method further comprises:

outputting, by the feature processing network, a prediction result corresponding to the character to be predicted based on the second feature information, the prediction result indicating a predicted character corresponding to the character to be predicted.

40. The method of any of claims 26 to 32, wherein the text processing network further comprises a feature processing network, the method further comprising:

and executing translation operation on the first character based on the second characteristic information through the characteristic processing network to obtain a translated first character, wherein the translated first character and the first character are different languages.

41. An apparatus for training a neural network, the apparatus comprising:

the acquisition module is used for acquiring a first training text, and the first training text comprises at least two second characters;

the input module is used for inputting the first training text into a first feature extraction network so as to perform feature extraction on the first training text through the first feature extraction network to obtain first feature information corresponding to the at least two second characters, wherein the first feature information comprises feature information of the second characters in at least one dimension;

the processing module is used for calculating second similarity information of the at least two second characters at a dimension level according to first feature information corresponding to the at least two second characters through the first feature extraction network;

the processing module is further configured to generate second feature information corresponding to the at least two second characters according to the second similarity information through the first feature extraction network;

an output module, configured to output, through a first feature processing network, a generated processing result corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters, where the first feature processing network and the first feature extraction network belong to a first text processing network;

the processing module is further configured to perform iterative training on the first text processing network according to a correct processing result corresponding to the at least two second characters, a generated processing result, and a loss function until a preset condition is met.

42. A training apparatus as defined in claim 41, wherein the at least two second characters comprise at least one character to be predicted;

the output module is specifically configured to output, through the first feature processing network, a prediction result corresponding to the character to be predicted based on second feature information corresponding to the at least two second characters, where the prediction result indicates a predicted character corresponding to the character to be predicted;

the processing module is specifically configured to perform iterative training on the first text processing network according to a correct character, a predicted character and a loss function corresponding to the character to be predicted until a preset condition is met, and output a second feature extraction network, where the second feature extraction network is the trained first feature extraction network.

43. The training device of claim 42,

the obtaining module is further configured to obtain a second training text, where the second training text includes at least two third characters;

the input module is further configured to input a second training text into the second feature extraction network, so as to generate second feature information corresponding to the at least two third characters through the second feature extraction network;

the output module is further configured to perform a classification operation based on second feature information corresponding to the at least two third characters through a second feature processing network, and output indication information of prediction categories corresponding to the at least two third characters, where the second feature extraction network and the second feature processing network belong to a second text processing network;

the processing module is specifically configured to perform iterative training on the second text processing network according to the correct category corresponding to the second training text, the indication information of the prediction category, and the loss function until a preset condition is met, and output the trained second text processing network.

44. A training apparatus as defined in claim 41, wherein the at least two second characters comprise at least one character to be predicted;

the output module is specifically configured to output, through the first feature processing network, a prediction result corresponding to the character to be predicted based on second feature information corresponding to the at least two second characters and an auto-regression algorithm, where the prediction result indicates a predicted character corresponding to the character to be predicted;

the processing module is specifically configured to perform iterative training on the first text processing network according to the correct character, the predicted character and the loss function corresponding to the character to be predicted until a preset condition is met, and output the trained first text processing network.

45. The training device of claim 41,

the output module is specifically configured to execute a translation operation on the first training text through the first feature processing network according to a correctly translated text corresponding to the first training text and second feature information corresponding to the at least two second characters, and output a translated second character, where the translated second character is in a different language from the second character;

the processing module is specifically configured to perform iterative training on the first text processing network according to the correctly translated text, the translated second character, and the loss function until a preset condition is met, and output the trained first text processing network.

46. An execution device comprising a processor coupled to a memory, the memory storing program instructions that, when executed by the processor, perform the steps performed by the text processing network of any of claims 1 to 15 or perform the steps performed by the neural network of any of claims 21 to 25 that processes sequence data.

47. Training device, comprising a processor coupled to a memory, the memory storing program instructions which, when executed by the processor, implement the method of any of claims 16 to 20.

48. A computer-readable storage medium, characterized by a program which, when run on a computer, causes the computer to perform the steps performed by the text processing network according to any one of claims 1 to 15, or causes the computer to perform the method according to any one of claims 16 to 20, or causes the computer to perform the steps performed by the neural network processing sequence data according to any one of claims 21 to 25.

49. Circuitry comprising processing circuitry configured to perform the steps performed by the text processing network of any of claims 1 to 15, or configured to perform the method of any of claims 16 to 20, or configured to perform the steps performed by the neural network of any of claims 21 to 25, or to process sequence data.

Technical Field

The present application relates to the field of artificial intelligence, and in particular, to a method for text processing network, neural network training, and related devices.

Background

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making. At present, text processing based on deep learning (deep learning) neural network is a common application mode of artificial intelligence.

In the field of Natural Language Processing (NLP), Sequence Modeling (Sequence Modeling) refers to a process of efficiently encoding a text data into a representation (representation), that is, a process of extracting features from a text. A Recurrent Neural Network (RNN) is a common neural network used for feature extraction of text.

However, when the text to be processed includes a plurality of characters, each character can only be processed one by one through the recurrent neural network, which is relatively inefficient, and a neural network capable of processing the text more efficiently is urgently needed to be proposed.

Disclosure of Invention

The embodiment of the application provides a text processing network, a neural network training method and related equipment, wherein the text processing network can process two or more characters at one time, and a more efficient text processing network is provided; and the generated second characteristic information is fused with information with finer granularity, so that the precision of the whole text processing network is improved.

In order to solve the above technical problem, an embodiment of the present application provides the following technical solutions:

in a first aspect, an embodiment of the present application provides a text processing network, which may be used in the field of text processing in the field of artificial intelligence. The text processing network comprises a feature extraction network, and the feature extraction network comprises a similarity calculation module and a generation module. The similarity calculation module is used for receiving at least two input first characters and performing feature extraction on the at least two first characters to obtain first feature information corresponding to the at least two first characters, wherein the first feature information comprises feature information of the first characters in at least one dimension. The similarity calculation module receives an original representation corresponding to at least two first characters, the original representation of a first character is sequence data comprising d elements and is used for indicating that the sequence data of the first character can carry information of the first character in d dimensions, and one element in the sequence data of the first character can correspond to one dimension of the first character. And the similarity calculation module is also used for calculating first similarity information of the at least two first characters at a dimension level according to the first characteristic information. And the generating module is used for generating second characteristic information corresponding to the at least two first characters according to the first similarity information, and the difference between the second characteristic information and the first characteristic information is that the first similarity information is blended into the second characteristic information. In the implementation mode, the text processing network can process two or more characters at one time, so that a more efficient text processing network is provided; in addition, in the process of generating the feature information of the characters, the similarity information of the characters in the dimension level is combined, namely, the generated second feature information is fused with information with finer granularity, and the accuracy of the whole text processing network is improved.

In a possible implementation manner of the first aspect, the first feature information includes a first representation and a second representation, both of which are matrices, a column of data in the first representation includes feature information of at least two first characters in one dimension, and a column of data in the second representation includes feature information of at least two first characters in one dimension. The similarity calculation module is specifically configured to calculate a similarity between the column data in the first representation and the column data in the second representation to obtain first similarity information. The first similarity information includes similarities between the first representation and the second representation in each dimension, and the first similarity information may be specifically expressed as a d × d matrix, that is, the first similarity information may include d × d elements. Further, an element in the first similarity information located in an ith row and a jth column represents a similarity score between an ith dimension of the first representation and a jth dimension of the second representation, i is an integer not less than 1 and not more than d, and j is an integer not less than 1 and not more than d. In this implementation, the first representation and the second representation are determined as a matrix, and similarity information of the first representation and the second representation at a dimension level is obtained by calculating similarity between column data in the first representation and column data in the second representation, which is simple to operate.

In a possible implementation manner of the first aspect, the similarity calculation module is specifically configured to transpose the first representation, and calculate a similarity between row data in the transposed first representation and column data in the second representation to obtain the first similarity information. In the implementation mode, a specific implementation mode for generating the first similarity information is provided, and the combination degree of the scheme and a specific application scene is improved.

In a possible implementation manner of the first aspect, the similarity calculation module is specifically configured to: dot product the row data of the target row in the transposed first representation with the column data of the target column in the second representation, or, calculating Euclidean distance between row data of the target row in the first representation after the conversion and column data of the target column in the second representation, or, calculating the Manhattan distance between the row data of the target row in the first representation after the conversion and the column data of the target column in the second representation, or, calculating the mahalanobis distance between the row data of the target row in the first representation after the conversion and the column data of the target column in the second representation, or calculating cosine similarity between row data of the target row in the first representation and column data of the target column in the second representation after the conversion, to generate a similarity score between the row data of the target row in the translated first representation and the column data of the target column in the second representation, that is, to generate an element value in the first similarity information. The row data of the target row in the first representation after the conversion refers to any row data in a plurality of rows of data included in the first representation after the conversion, and the column data of the target column in the second representation refers to any column data in a plurality of columns of data included in the second representation.

In a possible implementation manner of the first aspect, the similarity calculation module is specifically configured to: dot product the column data of the target column in the first representation with the column data of the target column in the second representation; or, calculating a euclidean distance between the column data of the target column in the first representation and the column data of the target column in the second representation; or, calculating a manhattan distance between the column data of the target column in the first representation and the column data of the target column in the second representation; or, calculating a mahalanobis distance between the column data of the target column in the first representation and the column data of the target column in the second representation; alternatively, the cosine similarity between the column data of the target column in the first representation and the column data of the target column in the second representation is calculated to generate a similarity score between the column data of the target column in the first representation and the column data of the target column in the second representation, that is, one value of the first similarity information is generated. The column data of the target column in the first representation refers to any one column of data in the multiple columns of data included in the first representation, and the column data of the target column in the second representation refers to any one column of data in the multiple columns of data included in the second representation. In the implementation mode, various implementation modes for calculating the similarity between the column data in the first representation and the column data in the second representation are provided, and the implementation flexibility of the scheme is improved.

In a possible implementation manner of the first aspect, the similarity calculation module is specifically configured to perform linear transformation on an original representation corresponding to at least two first characters to obtain a first representation and a second representation. In this implementation, because the first representation and the second representation are used for performing similarity calculation of at least two first characters at a dimension level, an original representation of the first character may not be adapted to the similarity calculation of the dimension level, and a learnable linear transformation model is used to generate the first representation and the second representation, and the linear transformation model can adjust model parameters in a training stage, so that the first representation and the second representation can correctly reflect similarity information at the dimension level, so as to improve the accuracy of the first similarity information, thereby improving the accuracy of the whole text processing network.

In a possible implementation manner of the first aspect, the similarity calculation module is specifically configured to multiply the original representations of the at least two first characters by a first linear transformation matrix to obtain a first representation, and multiply the original representations of the at least two first characters by a second linear transformation matrix to obtain a second representation. The first linear transformation matrix may be specifically represented as a d × d matrix, the second linear transformation matrix may also be specifically represented as a d × d matrix, parameters in the first linear transformation matrix and the second linear transformation matrix are obtained through pre-training, and the parameters in the first linear transformation matrix and the second linear transformation matrix may be the same or different. In this implementation manner, the first representation and the second representation are generated by the first linear transformation matrix and the second linear transformation matrix, and in the training stage, the parameters in the first linear transformation matrix and the second linear transformation matrix can be adjusted respectively, that is, the adjustment process of the parameters in the first linear transformation matrix and the second linear transformation matrix is more flexible, so that the generated first representation and the generated second representation can reflect the similarity information of the dimension level more correctly, and the accuracy of the first similarity information is improved.

In a possible implementation manner of the first aspect, the similarity calculation module is specifically configured to perform convolution processing on the original representations corresponding to the at least two first characters twice respectively to obtain the first representation and the second representation. In the implementation mode, another generation scheme of the first representation and the second representation is provided, and the implementation flexibility of the scheme is improved.

In a possible implementation manner of the first aspect, the first feature information includes a third representation, the third representation includes feature information of at least two first characters, and the similarity calculation module is further configured to multiply the at least two first characters by a third linear transformation matrix to obtain the third representation. And the third representation is used for fusing the similarity of each first character at a dimension level to generate second characteristic information. The third linear transformation matrix may be represented as a d × d matrix, and parameters in the third linear transformation matrix are obtained by pre-training and may be the same as or different from parameters in the first linear transformation matrix and the second linear transformation matrix. And the generating module is specifically configured to perform fusion processing on the third representation and the first similarity information to generate second feature information. In the implementation mode, the word information of the first characters and the similarity information of the plurality of first characters at the dimension level are simultaneously fused in the second characteristic information, so that the precision of a text processing network is further improved; in addition, the third representation is generated when the first character is subjected to feature extraction for the first time, so that when the second feature information is generated, the generated third representation and the first similarity information can be directly utilized for fusion processing, and the processing efficiency of the whole text processing network is improved.

In a possible implementation manner of the first aspect, the number of the first characters is N, where N is an integer greater than or equal to 2, and the generating module is specifically configured to: and generating a third-order tensor representation according to the third representation and the first similarity information, and compressing the third-order tensor representation to obtain second characteristic information. The third-order tensor expression comprises N matrixes, each matrix in the N matrixes can be specifically expressed as a d x d matrix, each matrix corresponds to one first character, and the feature information of one first character and the similarity information of one first character in the dimension level are fused in one matrix. The second characteristic information may be embodied as a two-dimensional matrix. In the implementation manner, in the fusion process of the third representation and the first similarity information, the third representation is expanded to be represented by a third-order tensor, and then compression processing is performed.

In one possible implementation manner of the first aspect, the first similarity information and the third representation are both matrices. The generating module is specifically configured to perform tensor product operation on the column data in the first similarity information and the column data represented by the third representation to obtain third-order tensor representation, and the tensor product operation may be specifically represented as an outer product operation. Or the generating module is specifically configured to perform addition operation on the column data in the first similarity information and the column data of the third representation to obtain the third-order tensor representation. In the implementation mode, two specific implementation schemes for generating the third-order tensor expression are provided, and the implementation flexibility of the scheme is improved.

In a possible implementation manner of the first aspect, the third-order tensor representation includes N matrices, each matrix is a two-dimensional matrix of d × d, that is, the third-order tensor representation is an N × d × d third-order tensor. The generation module is specifically configured to perform compression processing on the third-order tensor expression along any one of two d directions of the third-order tensor expression, so as to flatten one d direction of the third-order tensor expression to obtain second feature information, where the second feature information is an N × d two-dimensional matrix. Wherein, the compression processing mode comprises any one of the following modes: convolution, addition, averaging, taking the maximum or taking the minimum. In the implementation mode, various specific implementation schemes for generating the second characteristic information are provided, and the implementation flexibility of the scheme is improved.

In a possible implementation manner of the first aspect, the generating module is specifically configured to perform transposition processing on the third representation, and multiply the transposed third representation by the first similarity information to obtain the second feature information. In the implementation mode, the third representation after the conversion is multiplied by the first similarity information to directly obtain the second characteristic information, the implementation mode is simple, the calculation is convenient, and the time complexity is low; and a scheme for generating the second characteristic information is provided, so that the realization flexibility of the scheme is further enhanced.

In a possible implementation manner of the first aspect, the text processing network further includes a feature processing network, and the feature processing network is configured to perform a classification operation based on the second feature information, and output indication information of prediction categories corresponding to the at least two first characters, where the classification of the categories is based on semantics of the characters or the classification of the categories is based on parts of speech of the characters. In the implementation mode, the method and the device fall to the specific application scene of text classification, and the combining capacity of the scheme and the application scene is improved.

In a possible implementation manner of the first aspect, the at least two first characters include a character to be predicted, and the text processing network further includes a feature processing network, where the feature processing network is configured to output a prediction result corresponding to the character to be predicted based on the second feature information, and the prediction result indicates a predicted character corresponding to the character to be predicted. The prediction result may be expressed as an index number corresponding to the predicted character. In the implementation mode, the method and the device can be applied to an application scene of text classification and an application scene of text prediction, and the application scene of the scheme is expanded.

In a possible implementation manner of the first aspect, the text processing network further includes a feature processing network, where the feature processing network is configured to perform a translation operation on the first character based on the second feature information, and output indication information of the translated first character, where the indication information of the translated first character is used to indicate the translated first character, and the translated first character and the first character are in different languages. The indication information of the translated first character may be specifically expressed as an index number corresponding to the translated first character one to one. In the implementation mode, the method and the device can be applied to application scenes of text classification and text prediction and application scenes of text translation, and application scenes of the scheme are further expanded.

In a second aspect, an embodiment of the present application provides a training method for a neural network, which can be used in the field of text processing in the field of artificial intelligence. The method can comprise the following steps: the training equipment acquires a first training text, wherein the first training text comprises at least two second characters. The training equipment inputs the first training text into the first feature extraction network, so that feature extraction is performed on the first training text through the first feature extraction network to obtain first feature information corresponding to the at least two second characters, wherein the first feature information comprises feature information of the second characters in at least one dimension. The training equipment calculates second similarity information of the at least two second characters at a dimension level according to first feature information corresponding to the at least two second characters through a first feature extraction network; and the training equipment generates second feature information corresponding to at least two second characters according to the second similarity information through the first feature extraction network. The training equipment outputs a generation processing result corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters through a first feature processing network, and the first feature processing network and a first feature extraction network belong to a first text processing network. And the training equipment carries out iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result and the loss function until a preset condition is met. The preset condition may be that a convergence condition of the loss function is satisfied, or that the iteration number of the iterative training satisfies a preset number. The loss function may be embodied as a 0-1 loss function, a cross-entropy loss function, a logarithmic loss function, or other type of loss function.

In the implementation mode, the trained first text processing network can process two or more characters at one time, namely, a more efficient text processing network is provided; in addition, in the process of generating the feature information of the characters, the trained first text processing network can combine the similarity information of the characters in the dimension level, that is, the generated second feature information can be fused with information of finer granularity, which is beneficial to improving the precision of the whole text processing network.

In one possible implementation manner of the first aspect, the at least two second characters include at least one character to be predicted. The training device outputs, through the first feature processing network, a result of generating processing corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters, which may include: the training device outputs a prediction result corresponding to the character to be predicted based on second feature information corresponding to the at least two second characters through the first feature processing network, the prediction result indicating a predicted character corresponding to the character to be predicted. The training device performs iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result, and the loss function until a preset condition is met, and may include: the training equipment carries out iterative training on the first text processing network according to the correct character, the predicted character and the loss function corresponding to the character to be predicted until a preset condition is met, and outputs a second feature extraction network, wherein the second feature extraction network is the trained first feature extraction network. In the implementation mode, the first feature extraction network is placed in a scene of text prediction, and the first feature extraction network is iteratively trained, so that the feature extraction capability of the trained first feature extraction network is favorably improved due to the fact that the capability requirement on the feature extraction network in the scene of text prediction is high.

In one possible implementation manner of the first aspect, the method may further include: the training device obtains a second training text, wherein the second training text comprises at least two third characters. The training equipment inputs a second training text into a second feature extraction network so as to generate second feature information corresponding to at least two third characters through the second feature extraction network; the training equipment executes classification operation based on second feature information corresponding to at least two third characters through a second feature processing network, and outputs indication information of prediction categories corresponding to the at least two third characters, wherein the second feature extraction network and the second feature processing network belong to the second text processing network. And the training equipment carries out iterative training on the second text processing network according to the correct category corresponding to the second training text, the indication information of the prediction category and the loss function until preset conditions are met, and outputs the trained second text processing network. In the implementation mode, the feature extraction network in the text classification network is trained firstly, and then the whole text classification network is trained, so that semantic information learned in the primary training process can be effectively transferred to the whole text classification network, and the accuracy of the classification network after training is improved.

In one possible implementation manner of the first aspect, the at least two second characters include at least one character to be predicted. The training device outputs, through the first feature processing network, a result of generating processing corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters, which may include: the training device outputs a prediction result corresponding to the character to be predicted based on second feature information corresponding to the at least two second characters and an autoregressive algorithm through the first feature processing network, the prediction result indicating a predicted character corresponding to the character to be predicted. The autoregressive algorithm is that only a prediction result corresponding to one character to be predicted is generated in each prediction operation. The training device performs iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result, and the loss function until a preset condition is met, and may include: and the training equipment carries out iterative training on the first text processing network according to the correct character, the predicted character and the loss function corresponding to the character to be predicted until a preset condition is met, and outputs the trained first text processing network. In the implementation mode, the training method when the text processing network is used for text prediction is provided, and the application scene of the scheme is expanded.

In one possible implementation manner of the first aspect, the outputting, by the training device through the first feature processing network, a generation processing result corresponding to at least two second characters based on second feature information corresponding to the at least two second characters includes: and the training equipment executes translation operation on the first training text through a first feature processing network according to the correct translation text corresponding to the first training text and second feature information corresponding to at least two second characters, and outputs the translated second characters, wherein the translated second characters are in different languages from the second characters. The training equipment carries out iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result and the loss function until preset conditions are met, and the iterative training comprises the following steps: and the training equipment carries out iterative training on the first text processing network according to the correctly translated text, the translated second character and the loss function until a preset condition is met, and outputs the trained first text processing network. In the implementation mode, the training method when the text processing network is used for text translation is provided, and the application scene of the scheme is further expanded.

For specific implementation steps of the second aspect and various possible implementation manners of the second aspect in the embodiment of the present application, reference may be made to descriptions in the various possible implementation manners of the first aspect, and details are not repeated here.

In a third aspect, the present application provides a neural network for processing sequence data, which can be used in the field of processing sequence data in the field of artificial intelligence. The neural network comprises a feature extraction network, and the feature extraction network comprises a similarity calculation module and a generation module. And the similarity calculation module is used for receiving the input at least two sequence data and performing feature extraction on the at least two sequence data to obtain third feature information corresponding to the at least two sequence data, wherein the third feature information comprises feature information of the at least two sequence data in at least one dimension. And the similarity calculation module is also used for calculating third similarity information of the at least two sequence data in the dimension level according to the third characteristic information. And the generating module is used for generating fourth characteristic information corresponding to the at least two sequence data according to the third similarity information.

In one possible implementation of the third aspect, the sequence data is indicative of any one of: character information, consumption information, location information, and genetic information.

The third aspect of the embodiment of the present application may further perform steps in various possible implementation manners of the first aspect, and for specific implementation steps of the third aspect and various possible implementation manners of the third aspect of the embodiment of the present application and beneficial effects brought by each possible implementation manner, reference may be made to descriptions in various possible implementation manners of the first aspect, and details are not repeated here.

In a fourth aspect, an embodiment of the present application provides a text processing method, which can be used in the field of text processing in the field of artificial intelligence. The method can comprise the following steps: inputting at least two first characters into a text processing network, wherein the text processing network comprises a feature extraction network, and the feature extraction network comprises a similarity calculation module and a generation module; performing feature extraction on the at least two first characters through a similarity calculation module to obtain first feature information corresponding to the at least two first characters, wherein the first feature information comprises feature information of the first characters in at least one dimension; calculating first similarity information of at least two first characters at a dimension level according to the first feature information through a similarity calculation module; and generating second characteristic information corresponding to at least two first characters according to the first similarity information through a generating module.

The fourth aspect of the embodiment of the present application may further perform steps in each possible implementation manner of the first aspect, and for specific implementation steps of the fourth aspect and each possible implementation manner of the fourth aspect of the embodiment of the present application and beneficial effects brought by each possible implementation manner, reference may be made to descriptions in each possible implementation manner of the first aspect, and details are not repeated here.

In a fifth aspect, an embodiment of the present application provides a training apparatus for a neural network, which can be used in the field of text processing in the field of artificial intelligence. The device comprises: the acquisition module is used for acquiring a first training text, and the first training text comprises at least two second characters; the input module is used for inputting the first training text into a first feature extraction network so as to perform feature extraction on the first training text through the first feature extraction network to obtain first feature information corresponding to at least two second characters, wherein the first feature information comprises feature information of the second characters in at least one dimension; the processing module is used for calculating second similarity information of the at least two second characters at a dimension level according to first feature information corresponding to the at least two second characters through a first feature extraction network; the processing module is further used for generating second feature information corresponding to at least two second characters according to the second similarity information through the first feature extraction network; the output module is used for outputting a generation processing result corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters through a first feature processing network, and the first feature processing network and the first feature extraction network belong to a first text processing network; and the processing module is also used for performing iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result and the loss function until a preset condition is met.

The fifth aspect of the embodiment of the present application may further perform steps in each possible implementation manner of the second aspect, and for specific implementation steps of the fifth aspect and each possible implementation manner of the fifth aspect of the embodiment of the present application and beneficial effects brought by each possible implementation manner, reference may be made to descriptions in each possible implementation manner of the second aspect, and details are not repeated here.

In a sixth aspect, embodiments of the present application provide an execution device, which may include a processor, a memory coupled to the processor, the memory storing program instructions, which when executed by the processor, implement the steps performed by the text processing network according to the first aspect, or implement the steps performed by the neural network for processing sequence data according to the third aspect.

In a seventh aspect, an embodiment of the present application provides an execution device, which may include a processor, a processor coupled to a memory, and the memory storing program instructions, where the program instructions stored in the memory are executed by the processor to implement the neural network training method according to the second aspect.

In an eighth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored, and when the computer program runs on a computer, the computer program causes the computer to execute the steps executed by the text processing network according to the first aspect, or causes the computer to execute the training method for the neural network according to the second aspect, or causes the computer to execute the steps executed by the neural network for processing sequence data according to the third aspect.

In a ninth aspect, the present invention provides a circuit system, which includes a processing circuit configured to execute the steps executed by the text processing network according to the first aspect, or execute the training method of the neural network according to the second aspect, or execute the steps executed by the neural network according to the third aspect.

In a tenth aspect, embodiments of the present application provide a computer program, which when run on a computer, causes the computer to perform the steps performed by the text processing network according to the first aspect, or perform the method for training a neural network according to the second aspect, or perform the steps performed by the neural network for processing sequence data according to the third aspect.

In an eleventh aspect, embodiments of the present application provide a chip system, which includes a processor, configured to implement the functions recited in the above aspects, for example, to transmit or process data and/or information recited in the above methods. In one possible design, the system-on-chip further includes a memory for storing program instructions and data necessary for the server or the communication device. The chip system may be formed by a chip, or may include a chip and other discrete devices.

Drawings

FIG. 1 is a schematic structural diagram of an artificial intelligence body framework provided by an embodiment of the present application;

FIG. 2 is a system architecture diagram of a system for processing sequence data provided by an embodiment of the present application;

fig. 3 is a schematic flowchart of a text processing method according to an embodiment of the present application;

fig. 4 is a schematic diagram of a first representation in a text processing method according to an embodiment of the present application;

fig. 5 is a schematic diagram of first similarity information in a text processing method according to an embodiment of the present application;

fig. 6 is a schematic flowchart of generating third-order tensors in a text processing method according to an embodiment of the present application;

fig. 7 is a schematic diagram illustrating second feature information generated in the text processing method according to the embodiment of the present application;

fig. 8 is a schematic structural diagram of a text processing network according to an embodiment of the present application;

fig. 9 is another schematic flowchart of a text processing method according to an embodiment of the present application;

fig. 10 is a schematic flowchart of another text processing method according to an embodiment of the present application;

fig. 11 is a schematic structural diagram of a text processing network according to an embodiment of the present application;

fig. 12 is a schematic flowchart of a training method of a neural network according to an embodiment of the present application;

fig. 13 is a schematic flowchart of another method for training a neural network according to an embodiment of the present disclosure;

fig. 14 is a schematic flowchart of a training method of a neural network according to an embodiment of the present application;

FIG. 15 is a schematic flow chart of a method for processing sequence data according to an embodiment of the present disclosure;

fig. 16 is a schematic flowchart of a training method of a neural network according to an embodiment of the present application;

fig. 17 is a schematic structural diagram of a text processing network according to an embodiment of the present application;

FIG. 18 is a schematic structural diagram of an apparatus for training a neural network according to an embodiment of the present disclosure;

fig. 19 is a schematic structural diagram of an execution device according to an embodiment of the present application;

FIG. 20 is a schematic structural diagram of a training apparatus provided in an embodiment of the present application;

fig. 21 is a schematic structural diagram of a chip according to an embodiment of the present disclosure.

Detailed Description

Embodiments of the present application are described below with reference to the accompanying drawings. As can be known to those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solution provided in the embodiments of the present application is also applicable to similar technical problems.

The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the various embodiments of the application and how objects of the same nature can be distinguished. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

The general workflow of the artificial intelligence system will be described first, please refer to fig. 1, which shows a schematic structural diagram of an artificial intelligence body framework, and the artificial intelligence body framework is explained below from two dimensions of "intelligent information chain" (horizontal axis) and "IT value chain" (vertical axis). Where "intelligent information chain" reflects a list of processes processed from the acquisition of data. For example, the general processes of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision making and intelligent execution and output can be realized. In this process, the data undergoes a "data-information-knowledge-wisdom" refinement process. The 'IT value chain' reflects the value of the artificial intelligence to the information technology industry from the bottom infrastructure of the human intelligence, information (realization of providing and processing technology) to the industrial ecological process of the system.

(1) Infrastructure

The infrastructure provides computing power support for the artificial intelligent system, realizes communication with the outside world, and realizes support through a foundation platform. Communicating with the outside through a sensor; the computing power is provided by an intelligent chip, which includes but is not limited to hardware acceleration chips such as a Central Processing Unit (CPU), an embedded neural Network Processor (NPU), a Graphics Processing Unit (GPU), an Application Specific Integrated Circuit (ASIC), and a Field Programmable Gate Array (FPGA); the basic platform comprises distributed computing framework, network and other related platform guarantees and supports, and can comprise cloud storage and computing, interconnection and intercommunication networks and the like. For example, sensors and external communications acquire data that is provided to intelligent chips in a distributed computing system provided by the base platform for computation.

(2) Data of

Data at the upper level of the infrastructure is used to represent the data source for the field of artificial intelligence. The data relates to graphs, images, voice and texts, and also relates to the data of the Internet of things of traditional equipment, including service data of the existing system and sensing data such as force, displacement, liquid level, temperature, humidity and the like.

(3) Data processing

Data processing typically includes data training, machine learning, deep learning, searching, reasoning, decision making, and the like.

The machine learning and the deep learning can perform symbolized and formalized intelligent information modeling, extraction, preprocessing, training and the like on data.

Inference means a process of simulating an intelligent human inference mode in a computer or an intelligent system, using formalized information to think about and solve a problem by a machine according to an inference control strategy, and a typical function is searching and matching.

The decision-making refers to a process of making a decision after reasoning intelligent information, and generally provides functions of classification, sequencing, prediction and the like.

(4) General capabilities

After the above-mentioned data processing, further based on the result of the data processing, some general capabilities may be formed, such as algorithms or a general system, e.g. translation, analysis of text, computer vision processing, speech recognition, etc.

(5) Intelligent product and industrial application

The intelligent product and industry application refers to the product and application of an artificial intelligence system in various fields, and is the encapsulation of an artificial intelligence integral solution, the intelligent information decision is commercialized, and the landing application is realized, and the application field mainly comprises: intelligent terminal, intelligent manufacturing, intelligent transportation, intelligent house, intelligent medical treatment, intelligent security protection, autopilot, safe city etc..

The embodiment of the application can be applied to various fields of artificial intelligence, and particularly can be applied to various scenes needing to process sequence data, wherein the sequence data refers to an ordered set consisting of a plurality of elements. As an example, character information may be indicated by sequence data, for example; the sequence data can also be used for reflecting consumption information of the user, wherein the consumption information comprises but is not limited to virtual products and physical products purchased by the user; the sequence data is also used to indicate the location information of the user, and the sequence data may also be used to indicate gene information, etc., it should be understood that the examples herein are merely for convenience of understanding the application scenarios of the embodiments of the present application, and the application scenarios of the embodiments of the present application are not exhaustive. As follows, in the embodiment of the present application, a scenario in which sequence data is used to indicate character information in a text to be processed is taken as an example, a detailed description is given to a sequence data processing method provided in the embodiment of the present application, and then further application scenarios are described.

To facilitate understanding of the present solution, in the embodiment of the present application, first, a system for processing sequence data provided in the embodiment of the present application is described with reference to fig. 2, please refer to fig. 2, and fig. 2 is a system architecture diagram of the system for processing sequence data provided in the embodiment of the present application. In FIG. 2, a system 200 for processing sequence data includes an execution device 210, a training device 220, a database 230, and a data storage system 240, wherein the execution device 210 includes a calculation module 211 therein.

In the training phase, the database 230 stores a training data set, where the training data set may include a plurality of training samples and correct processing results corresponding to the training samples, and the training samples include at least two training data. The training device 220 generates a target model/rule 201 for processing the sequence data, and iteratively trains the target model/rule 201 using a set of training data in the database to obtain a mature target model/rule 201.

During the inference phase, the execution device 210 may invoke data, code, etc. from the data storage system 240 and may store data, instructions, etc. in the data storage system 240. The data storage system 240 may be configured in the execution device 210, or the data storage system 240 may be an external memory with respect to the execution device 210. The calculation module 211 may process at least two sequence data input by the execution device 210 through the mature target model/rule 201 to obtain similarity information of the at least two sequence data at a dimension level, and generate a processing result based on the similarity information at the dimension level, where a concrete representation form of the processing result is related to a function of the target model/rule 201.

In some embodiments of the present application, for example, in fig. 2, a "user" may interact directly with the execution device 210, that is, the execution device 210 and the client device are integrated in the same device. However, fig. 2 is only a schematic architecture diagram of two image processing systems provided by the embodiment of the present invention, and the positional relationship between the devices, modules, etc. shown in the diagram does not constitute any limitation. In other embodiments of the present application, the execution device 210 and the client device may be separate devices, the execution device 210 is configured with an input/output interface to perform data interaction with the client device, the "user" may input the captured image to the input/output interface through the client device, and the execution device 210 returns the processing result to the client device through the input/output interface.

As can be seen from the description in fig. 2, the embodiment of the present application includes an inference phase and a training phase, and the flow of the inference phase and the training phase are different, and the inference phase and the training phase are described below separately.

First, reasoning phase

In the embodiment of the present application, in the scenario that the sequence data is used to indicate character information in the text to be processed, the inference phase describes how the execution device 210 processes the text to be processed by using a mature text processing network. Specifically, in the large application scenario of text processing, to include understanding text information, performing text prediction, text translation, or other specific application scenarios, the three application scenarios of the foregoing example are described below.

(1) Applied to the scene of understanding text

In the context of understanding the text, the tasks of the text processing network include, but are not limited to, classifying the entire text, labeling words in the text (which may also be referred to as sequence labeling), or other tasks of natural language understanding. Referring to fig. 3, fig. 3 is a schematic flow chart of a text processing method according to an embodiment of the present application, where the text processing method according to the embodiment of the present application may include:

301. the execution device enters at least two first characters into the text processing network.

In some embodiments of the present application, the execution device obtains a text to be processed, where the text to be processed includes at least two first characters, and the execution device inputs the entire text to be processed into the text processing network. Specifically, the execution device may store a character table, where the character table includes original representations of a plurality of characters, and after acquiring at least two first characters included in the text to be processed, the execution device may acquire the original representation of each first character from the character table. The original representation of a first character is a sequence data, which is used to indicate that the sequence data of the first character will carry information of the first character in at least one dimension. For example, the sequence data may be embodied as a vector including d elements, and the d elements in the vector correspond to d dimensions of the first character respectively; adding the original representations of each first character together may result in a matrix corresponding to all the first characters in the text to be processed, for example, the text to be processed includes N first characters, and the original representation corresponding to at least one first character included in the text to be processed may be an N × d matrix. Where d is an integer of 1 or more, for example, d is 1, 2, 3, 4, 5, 6 or other values, and N is an integer of 2 or more, for example, N is 2, 3, 5, 10, 20, 50 or other values. Optionally, the execution device adds a [ CLS ] character to the beginning of at least two first characters included in the text to be processed, where the [ CLS ] character can be understood as a header character, and the feature information corresponding to the [ CLS ] character is used for reflecting the feature of the whole text to be processed.

The text processing network comprises a feature extraction network and a feature processing network. The feature extraction network comprises a similarity calculation module and a generation module; the feature processing network in the application scenario can be embodied as a classifier, and the classifier outputs a label. Further, the classifier may be embodied as a perceptron, or the classifier includes a linear transformation matrix and a normalized exponential (softmax) function, and the classifier may also be embodied in other forms, which is not limited herein.

302. The executing equipment performs feature extraction on the at least two first characters through the similarity calculation module to obtain first feature information corresponding to the at least two first characters.

In the embodiment of the application, after the executing device inputs the original representation corresponding to the text to be processed into the text processing network, the executing device may perform feature extraction on at least two first characters in the text to be processed through a similarity calculation module of the text processing network to obtain first feature information corresponding to the at least two first characters.

Wherein the first feature information includes feature information of the first character in at least one dimension. The first feature information may include a first representation and a second representation, the first representation and the second representation may each include feature information of all the first characters in each dimension, and the feature information carried in the first representation and the second representation is used to calculate similarity of each first character in the dimension level. Further, the first representation and the second representation may be specifically expressed as a matrix, each row in the matrix corresponds to one first character, each row in the matrix carries feature information of one first character in each dimension, and each column in the matrix carries feature information of all first characters in one dimension.

Optionally, the first feature information may further include a third representation, and the meaning and the concrete representation of the third representation are similar to those of the first representation and the second representation, except that the third representation is used differently from the first representation and the second representation, and the third representation is used for being fused with the similarity of each first character at the dimension level to generate the second feature information.

Specifically, in one implementation, the execution device performs linear transformation on the original representations corresponding to the at least two first characters through the similarity calculation module to obtain the first representation and the second representation. Optionally, the executing device performs linear transformation on the original representations corresponding to the at least two first characters through the similarity calculation module to obtain the first representation, the second representation and the third representation.

More specifically, the similarity calculation module may include a first linear transformation matrix and a second linear transformation matrix, each being W_AAnd W_B. The execution device passes W_APerforming linear transformation processing on the matrix T (namely, the original representations corresponding to the at least two first characters) to obtain a matrix A (namely, a first representation); further, W_AThe matrix a may be embodied as an N × d matrix, that is, the first representation includes feature information of each of the N first characters in d dimensions. The execution device passes W_BCarrying out linear transformation processing on the original representations corresponding to the at least two first characters to obtain a matrix B (namely a second representation); further, W_BThe matrix B may be embodied as a d × d matrix, and the matrix B may be embodied as an N × d matrix, that is, the second representation includes feature information of each of the N first characters in d dimensions. It should be noted that, in practical cases, the parameters in the first linear transformation matrix and the second linear transformation matrix may be the same or different, and there is a possibility that the matrix a and the matrix T are the same, that is, there is a possibility that the transformed first representation and the original representation are the same, and there are momentsThe likelihood that the matrix B is the same as the matrix T, i.e. that the transformed second representation is the same as the original representation, is present, both depending on the training result of the training phase. In this implementation manner, the first representation and the second representation are generated by the first linear transformation matrix and the second linear transformation matrix, and in the training stage, the parameters in the first linear transformation matrix and the second linear transformation matrix can be adjusted respectively, that is, the adjustment process of the parameters in the first linear transformation matrix and the second linear transformation matrix is more flexible, so that the generated first representation and the generated second representation can reflect the similarity information of the dimension level more correctly, and the accuracy of the first similarity information is improved.

To further understand the first representation and the second representation, please refer to fig. 4, fig. 4 is a schematic diagram of the first representation in the text processing method provided in the embodiment of the present application, and a value of d is 4 and a value of N is 5 in fig. 4 as an example. Where a1 refers to a row of data in the first representation (i.e., matrix a in fig. 4), a1 refers to feature information of a first character, where the row of data includes 4 elements, each element refers to feature information of the first character in one dimension, a1 refers to feature information of the first character in 4 dimensions, a2 refers to a column of data in the first representation, a2 includes feature information of 5 first characters in one dimension, and matrix a includes feature information of each of 5 first characters in 4 dimensions, it should be understood that the example in fig. 4 is merely for convenience of understanding of the present solution and is not used for limiting the present solution.

Optionally, a third linear transformation matrix (i.e., W) may be included in the similarity calculation module_C) The executive device passes W_CPerforming linear transformation processing on the original representations corresponding to the at least two first characters to obtain a matrix C (namely a third representation); further, W_CSpecifically, the matrix C may also be expressed as a d × d matrix, and specifically, the matrix C may also be expressed as an N × d matrix, that is, the third representation includes feature information of each of the N first characters in d dimensions. The parameters of the third linear transformation matrix and the first and second linear transformation matrices may be the same or different, and there are matrices C andthe likelihood that the matrix T is the same, i.e. there is a likelihood that the transformed third representation is the same as the original representation, is also dependent on the training result of the training phase.

In the embodiment of the application, because the first representation and the second representation are used for calculating the similarity of at least two first characters at a dimension level, the original representation of the first characters cannot necessarily adapt to the similarity calculation mode of the dimension level, and the first representation and the second representation are generated by utilizing a learnable linear transformation model which can adjust model parameters at a training stage, so that the first representation and the second representation can correctly reflect the similarity information at the dimension level, the accuracy of the first similarity information is improved, and the precision of the whole text processing network is improved.

In another implementation manner, the similarity calculation module may include convolution layers, and the execution device performs convolution processing on the original representations corresponding to the at least two first characters twice through the convolution layers of the similarity calculation module to obtain the first representation and the second representation. Optionally, the executing device performs convolution processing on the original representations corresponding to the at least two first characters three times respectively through the convolution layer of the similarity calculation module to obtain a first representation, a second representation and a third representation.

In the embodiment of the application, another generation scheme of the first representation and the second representation is provided, and the realization flexibility of the scheme is improved.

303. The executing equipment calculates first similarity information of the at least two first characters at the dimension level according to the first characteristic information through a similarity calculation module.

In this embodiment of the application, the first feature information includes a first representation and a second representation, both of which may be specifically expressed as a matrix, a column of data in the first representation includes feature information of at least two first characters in one dimension, and a column of data in the second representation includes feature information of at least two first characters in one dimension.

Step 303 may specifically include: the execution device calculates the similarity between the column data in the first representation and the column data in the second representation through a similarity calculation module to obtain first similarity information. The first similarity information includes similarities between the first representation and the second representation in each dimension, and the first similarity information may be specifically expressed as a d × d matrix S, that is, the first similarity information may include d × d elements. Further, an element of the d × d elements located in the ith row and jth column represents a similarity score between the ith dimension of the first representation and the jth dimension of the second representation. Wherein i is an integer greater than or equal to 1 and less than or equal to d, and j is an integer greater than or equal to 1 and less than or equal to d.

To more intuitively understand the present disclosure, please refer to fig. 5, and fig. 5 is a schematic diagram of first similarity information in a text processing method according to an embodiment of the present disclosure. Taking the value of d as 4 in fig. 5 as an example, where the matrix S represents the entire first similarity information, and B1 represents the similarity score between the second column of data of the first representation (i.e. the second-dimension feature information of all the first characters in the first representation) and the third column of data of the second representation (i.e. the third-dimension feature information of all the first characters in the second representation), it should be understood that the example in fig. 5 is only for convenience of understanding of the present solution, and is not used to limit the present solution.

In the embodiment of the application, the first representation and the second representation are determined as matrixes, the similarity information of the first representation and the second representation at the dimension level is obtained by calculating the similarity between the column data in the first representation and the column data in the second representation, and the operation is simple.

More specifically, in one case, the execution device transposes the first representation by the similarity calculation module, and calculates a similarity between the line data in the transposed first representation and the column data in the second representation to obtain the first similarity information. In the implementation mode, a specific implementation mode for generating the first similarity information is provided, and the combination degree of the scheme and a specific application scene is improved.

Further, the execution device may perform the conversion of the row data of the target row in the first representation and the target column in the second representation by the similarity calculation moduleThe column data of (a) are dot-multiplied to generate a similarity score between the row data of the target row in the transposed first representation and the column data of the target column in the second representation, i.e. one value of the first similarity information in the form of a matrix is generated. The row data of the target row in the first representation after the conversion refers to any row data in a plurality of rows of data included in the first representation after the conversion, and the column data of the target column in the second representation refers to any column data in a plurality of columns of data included in the second representation. The execution device repeatedly performs the aforementioned operations to generate a similarity score between the row data of each row in the transposed first representation and the column data of each column in the second representation, thereby obtaining each value in the first similarity information in a matrix form. That is, the first similarity information can be obtained by formula A^TB is obtained, where a refers to the matrix a (i.e. the first representation) and B refers to the matrix B (i.e. the second representation).

The execution device may also calculate, by the similarity calculation module, a euclidean distance between row data of the target row in the translated first representation and column data of the target column in the second representation; or calculating the Manhattan distance between the row data of the target row in the first representation after the conversion and the column data of the target column in the second representation; or calculating the mahalanobis distance between the row data of the target row in the transposed first representation and the column data of the target column in the transposed second representation; or calculating cosine similarity between row data of the target row in the first representation after the conversion and column data of the target column in the second representation, and the like, so as to generate a similarity score between the row data of the target row in the first representation after the conversion and the column data of the target column in the second representation. It should be noted that the executing device may further perform other operations through the similarity calculation module to generate a similarity score between the row data of the target row in the translated first representation and the column data of the target column in the second representation, which is not limited herein. The executing device repeatedly executes the operations to generate similarity scores between the row data of each row in the first representation and the column data of each column in the second representation after conversion, so that first similarity information is obtained.

In another implementation manner, the execution device directly calculates the similarity between the column data of the first representation and the column data of the second representation through the similarity calculation module to obtain the first similarity information.

Further, the execution device may perform dot product of the column data of the target column in the first representation and the column data of the target column in the second representation by the similarity calculation module; or, calculating a euclidean distance between the column data of the target column in the first representation and the column data of the target column in the second representation; or, calculating a manhattan distance between the column data of the target column in the first representation and the column data of the target column in the second representation; or, calculating a mahalanobis distance between the column data of the target column in the first representation and the column data of the target column in the second representation; alternatively, the cosine similarity or the like between the column data of the target column in the first representation and the column data of the target column in the second representation is calculated to generate a similarity score between the column data of the target column in the first representation and the column data of the target column in the second representation, that is, one value of the first similarity information in the form of a matrix is generated. The column data of the target column in the first representation refers to any one column of data in the multiple columns of data included in the first representation, and the column data of the target column in the second representation refers to any one column of data in the multiple columns of data included in the second representation. It should be noted that the executing device may further perform other operations through the similarity calculation module to generate a similarity score between the column data of the target column in the first representation and the column data of the target column in the second representation, which is not limited herein. The execution device repeatedly performs the aforementioned operations to generate a similarity score between the column data of each column in the transposed first representation and the column data of each column in the second representation, thereby obtaining each value in the first similarity information in the form of a matrix.

In the embodiment of the application, various implementation modes for calculating the similarity between the column data in the first representation and the column data in the second representation are provided, and the implementation flexibility of the scheme is improved.

As can be seen from the description in step 303, in the generation process of the first similarity information, d is executed in total²Minor operation, each operation being longThe operation between two vectors with degree N, so the time complexity for obtaining the first similarity information is calculated to be O (Nd)²)。

304. And the execution equipment generates second characteristic information corresponding to at least two first characters according to the first similarity information through a generation module.

In the embodiment of the application, the feature extraction network of the text processing network further comprises a generating module besides the similarity calculating module, the similarity calculating module is used for calculating first similarity information of at least two first characters at a dimension level, and the generating module is used for generating second feature information corresponding to the at least two first characters according to the first similarity information. After the execution device generates the first similarity information, second feature information corresponding to at least two first characters can be generated according to the first similarity information through a generation module of the text processing network. The second characteristic information is different from the first characteristic information in that first similarity information is blended into the second characteristic information; the second feature information may be specifically represented as a two-dimensional matrix, and further, the two-dimensional matrix may be an N × d matrix, that is, the two-dimensional matrix includes N rows, each row includes d elements, and each row of data in the two-dimensional matrix includes feature information of one first character in d dimensions.

Specifically, in one case, if the first feature information includes the third representation, the execution device may perform fusion processing on the third representation and the first similarity information through the generation module to generate the second feature information.

In the embodiment of the application, the word information of the first characters and the similarity information of the plurality of first characters at the dimension level are simultaneously fused in the second characteristic information, so that the precision of a text processing network is further improved; in addition, the third representation is generated when the first character is subjected to feature extraction for the first time, so that when the second feature information is generated, the generated third representation and the first similarity information can be directly utilized for fusion processing, and the processing efficiency of the whole text processing network is improved.

More specifically, the number of first characters is N. In one implementation, the execution device generates, by the generation module, a third-order tensor representation according to the third representation and the first similarity information, where the third-order tensor representation includes N matrices, each matrix corresponds to one first character, the feature information of one first character and the similarity information of one first character at the dimension level are fused in one matrix, and each matrix in the N matrices may be specifically represented as a d × d matrix. Since the third-order tensor representation includes N matrices and the second eigen information is a two-dimensional matrix, after the execution device generates the third-order tensor representation through the generation module, the execution device needs to perform compression processing on the third-order tensor representation through the generation module to obtain the second eigen information. In the embodiment of the application, in the fusion process of the third representation and the first similarity information, the third representation is expanded to be the third-order tensor representation, and then compression processing is performed.

Further, the generation process for the third order tensor representation. In one implementation, the execution device performs tensor product operation on the column data in the first similarity information and the column data of the third representation through the generation module to generate a third-order tensor representation. Tensor product operations include, but are not limited to, outer products or other types of tensor product operations. Furthermore, since the first similarity information may be a d × d matrix, and the third representation may be an N × d matrix, that is, the first similarity information and the third representation are d columns, the execution device performs tensor product operation on the kth column data in the first similarity information and the kth column data in the third representation through the generation module to generate an N × d matrix, where k is an integer greater than or equal to 1 and less than or equal to d. The performing device repeats the foregoing operation d times to generate an nxdxd third order tensor representation. As can be seen from the foregoing description, the generation process of the third-order tensor expression includes d outer product operations, each outer product operation is performed by an outer product of a vector with a length d and a vector with a length N, so the time complexity of this step is O (Nd)²)。

To further understand the present solution, please refer to fig. 6, and fig. 6 is a schematic flowchart illustrating a process of generating a third order tensor in the text processing method according to the embodiment of the present application. In fig. 6, taking the first similarity information (i.e., the matrix S in fig. 6) as a 4 × 4 matrix, that is, d is 4, the third representation (i.e., the matrix C in fig. 6) is a 5 × 4 matrix, that is, N is 5, as an example, the execution device performs an outer product operation on the first column of data in the matrix S and the first column of data in the matrix C through the generation module to obtain a 5 × 4 matrix, the execution device performs an outer product operation on the second column of data in the matrix S and the second column of data in the matrix C through the generation module to obtain another 5 × 4 matrix, the execution device repeatedly performs the foregoing operations to obtain 45 × 4 matrices, and stacks the 45 × 4 matrices to obtain the third-order tensor representation, it should be understood that the example in fig. 6 is only to facilitate understanding of the generation process of the third-order tensor representation, and is not intended to limit the present solution.

In another implementation manner, the execution device performs addition operation on the column data in the first similarity information and the column data in the third representation through the generation module to obtain the third-order tensor representation. Furthermore, since the first similarity information may be a d × d matrix and the third representation may be an N × d matrix, that is, the first similarity information and the third representation are d columns, the execution device performs an addition operation on the kth column data in the first similarity information and the kth column data in the third representation through the generation module to generate an N × d matrix. That is, each of the d elements included in the k-th column of the first similarity information is added to each of the d elements included in the k-th column of the data in the third representation. The performing device repeats the foregoing operation d times to generate an nxdxd third order tensor representation. To further understand the present solution, as described with reference to fig. 6, the executing device adds each element in the first column of data in the matrix S to each element in the first column of data in the matrix C through the generating module to obtain a 5 × 4 matrix, and repeats the foregoing operations 4 times through the generating module to obtain 45 × 4 matrices, and stacks the 45 × 4 matrices to obtain the third-order tensor representation.

In the embodiment of the application, two specific implementation schemes for generating the third-order tensor expression are provided, and the implementation flexibility of the scheme is improved.

The process of compressing the third order tensor representation is targeted. After the execution device obtains the third-order tensor representation through the generation module, since the third-order tensor representation is an N × d × d third-order tensor, the execution device may perform compression processing on the third-order tensor representation along any one d direction of two d directions represented by the third-order tensor through the generation module to obtain second feature information, where the second feature information is an N × d two-dimensional matrix. Wherein, the compression processing mode comprises any one of the following modes: convolution, addition, averaging, maximum, minimum or other compression processing.

Furthermore, the third order tensor is expressed as an N × d × d third order tensor, and the third order tensor expression can also be regarded as an N × d matrix. When convolution is selected in a compression processing mode, the generation module may include d convolution kernels having a length of d and a width of 1, the execution device performs convolution on one column in one matrix of the N matrices represented by the third-order tensor through one convolution kernel of the d convolution kernels to obtain one value, and then performs convolution on one column in one matrix through the d convolution kernels respectively to obtain d values, that is, to obtain a vector representation corresponding to a first character, where the vector representation includes d elements. The execution device performs the foregoing operation on each of the N matrices, so that N vectors including d elements, that is, an N × d two-dimensional matrix, that is, the second feature information, can be obtained. The time complexity of this step is O (Nd)²)。

Since the third-order tensor expression can be an nxdxd third-order tensor, the third-order tensor expression can be seen as a d nxd two-dimensional matrix, and when the compression processing mode selects addition, averaging, maximum value or minimum value, the execution device performs addition, averaging, maximum value or minimum value operation along any one d direction of two d directions expressed by the third-order tensor, so that the d direction expressed by the third-order tensor is flattened, and an nxd two-dimensional matrix is obtained, that is, the second feature information is obtained.

In the embodiment of the application, various specific implementation schemes for generating the second characteristic information are provided, and the implementation flexibility of the scheme is improved.

In another implementation, the execution device performs transposition processing on the third representation through a generation module of the text processing network, multiplies the transposed third representation by the first similarity information to obtain the second feature information, and refers to the above description for specific descriptions of the third representation, the first similarity information, and the second feature information, which is not described in detail here. In the embodiment of the application, the second feature information is directly obtained by multiplying the converted third representation by the first similarity information, so that the implementation mode is simple, the calculation is convenient, and the time complexity is low; and a scheme for generating the second characteristic information is provided, so that the realization flexibility of the scheme is further enhanced.

In another case, if the first feature information does not include the third representation, the execution device may generate the third representation according to the original representations corresponding to the at least two first characters through the generation module, and a specific generation manner of the third representation may refer to the description in step 302, which is not described herein again. The execution device may further perform fusion processing on the third representation and the first similarity information through the generation module to generate the second feature information, and the specific implementation manner may refer to the above description, which is not described herein again.

To further understand the present disclosure, please refer to fig. 7, and fig. 7 is a schematic diagram illustrating the generation of the second feature information in the text processing method according to the embodiment of the present disclosure. As shown in fig. 7, the text to be processed is "XX mobile phone is good", the function of the whole text processing network is to generate a classification tag corresponding to the whole text to be processed, C1 refers to an original representation corresponding to the whole text to be processed, and C2 refers to second feature information. Using a linear transformation matrix W through a feature extraction network in a text processing network_APerforming linear transformation processing on C1 to obtain a first representation (i.e., matrix a in fig. 7); by passingFeature extraction network in a text processing network using a linear transformation matrix W_BPerforming linear transformation processing on C1 to obtain a second representation (i.e., matrix B in fig. 7); using a linear transformation matrix W through a feature extraction network in a text processing network_CThe third representation (i.e., matrix C in fig. 7) is obtained by performing a linear transformation process on C1. Through the feature extraction network, calculating first similarity information of at least two first characters at a dimension level according to the first representation and the second representation, and performing fusion processing on the third representation and the first similarity information to generate second feature information (i.e., C2 in fig. 7), it should be understood that the example in fig. 7 is only for convenience of understanding the scheme, and is not used to limit the scheme.

Optionally, after the execution device generates the second feature information, the execution device may further obtain, by a generation module of the text processing network, an original representation corresponding to the text to be processed (that is, at least two first characters), add, by the generation module of the text processing network, the original representation corresponding to the text to be processed and the second feature information, and perform layer normalization (layerormanization) to obtain fourth feature information.

305. The execution device executes a classification operation through the feature processing network, and outputs indication information of prediction categories corresponding to the at least two first characters.

In some embodiments of the present application, in the application scenario, the feature processing network may perform a classification operation based on the second feature information, and output information indicative of prediction categories corresponding to the at least two first characters. The classification of the category is based on the semantics of the character, or the classification of the category is based on the part of speech of the character. Optionally, the executing device may perform a classifying operation based on the fourth feature information through the feature processing network, and output the indication information of the prediction classes corresponding to the at least two first characters. If the task of the entire text processing network is to classify the entire text to be processed, for example, the text to be processed is "weather is good today", the output indication information indicates that the classification label of the entire text to be processed is weather. If the task of the whole text processing network is to perform sequence labeling on the text to be processed, for example, the text to be processed is "weather is good today", the output indication information indicates that the classification label corresponding to "weather" may be a noun, the classification label corresponding to "very" may be an adjective, and the classification label corresponding to "good weather" may be an adjective.

It should be noted that, in this embodiment of the application, the number of times of execution between steps 302 to 304 and step 305 is not limited, and the number of times of execution between steps 302 to 304 and step 305 may be more than one, that is, after the execution device executes step 304, the execution device may re-enter step 302, replace the original representation corresponding to the text to be processed with the second feature information corresponding to the text to be processed, or replace the original representation corresponding to the text to be processed with the fourth feature information corresponding to the text to be processed, and re-execute steps 302 to 304, so as to generate new second feature information. After repeating steps 302 to 304 at least twice, step 305 is entered, and the performing device performs a classifying operation according to the updated second feature information through the feature processing network, or performs a classifying operation according to the updated fourth feature information through the feature processing network. The number of execution times between steps 302 to 304 and step 305 may also be one to one, that is, after the second feature information is generated or the fourth feature information is generated by the generation module of the text processing network, step 305 is directly entered.

To further understand the present solution, please refer to fig. 8, and fig. 8 is a schematic structural diagram of a text processing network according to an embodiment of the present application. In fig. 8, the pending text is "weather today is good. ", and the function of the text processing network is to output a label for the entire text to be processed. As shown, the execution device adds [ CLS ] at the beginning of at least two first characters comprised by the text to be processed]Character, to which [ CLS ] is to be added]The text to be processed of the characters is input into a text processing network, one text processing network may include a plurality of feature extraction networks, and the steps performed by each feature extraction network may refer to the descriptions in steps 302 to 304, D1 refers to second feature information generated by the feature extraction network, and D2 refers to new second feature information generated by the feature extraction network. Wherein h is₁To h₉The whole can be represented as a matrix with 9 rows, that is, the whole matrix can include 9 vectors, each vector being associated with h₁To h₉One-to-one correspondence is realized; h is₂To h₉Each vector in the character is reflected by the characteristic information of the corresponding character, h₁Reflected is the information of the entire text to be processed. The execution device sends h through the feature extraction network₁Inputting into the feature processing network to make the feature processing network according to h₁And performing classification operation to obtain a classification label corresponding to the whole text to be processed as "weather", and it should be understood that the example in fig. 8 is only for convenience in understanding the scheme, and is not used for limiting the scheme.

In the embodiment of the application, the text classification method and the device can be used for falling to the specific application scene of text classification, so that the combining capacity of the scheme and the application scene is improved.

In the embodiment of the application, the feature extraction module is configured to receive at least two first characters, perform feature extraction on the at least two first characters to obtain first feature information, where the first feature information includes feature information of the first characters in at least one dimension, calculate first similarity information of the at least two first characters in the dimension level according to the first feature information, and further generate second feature information corresponding to the at least two characters according to the first similarity information, that is, the feature extraction module can process two or more characters at a time, thereby providing a more efficient text processing network; in addition, in the process of generating the feature information of the characters, the similarity information of the characters in the dimension level is combined, namely, the generated second feature information is fused with information with finer granularity, and the accuracy of the whole text processing network is improved.

(2) Application in scenes of text prediction

In an embodiment of the present application, please refer to fig. 9, where fig. 9 is a schematic flowchart of a text processing method provided in the embodiment of the present application, and the text processing method provided in the embodiment of the present application may include:

901. the execution device enters at least two first characters into the text processing network.

In some embodiments of the present application, the execution device obtains a to-be-processed text, where the to-be-processed text includes a true value character and a to-be-predicted character, and the execution device may replace the to-be-predicted character with a mask character, so as to obtain at least two first characters, and input the at least two first characters into a text processing network. For example, the to-be-processed text is "i like travel, wherein the favorite place is Yunnan, and there is a certain opportunity to go to XXXXXX later," and then the whole to-be-processed text includes 27 true characters and 4 characters to be predicted. The at least one first character input to the text processing network may be specifically expressed in a form of sequence data, and for the specific expression form of the at least two first characters, reference may be made to the description in step 301 in the corresponding embodiment of fig. 3, which is not repeated herein. The character to be predicted may specifically be replaced by a MASK character, which may also be referred to as a [ MASK ] character.

The text processing network comprises a feature extraction network and a feature processing network. The feature extraction network comprises a similarity calculation module and a generation module; the execution device in the application scenario may be configured with a character table, each character in the character table corresponds to a unique index number, the feature processing network in the application scenario may also be specifically expressed as a classifier, the classifier may output the index number, and the execution device determines the predicted character according to the index number and the character table. For the concrete expression forms of the similarity calculation module, the generation module and the classifier, reference may be made to the description in the embodiment corresponding to fig. 3, which is not repeated herein.

902. The executing equipment performs feature extraction on the at least two first characters through the similarity calculation module to obtain first feature information corresponding to the at least two first characters.

903. The executing equipment calculates first similarity information of the at least two first characters at the dimension level according to the first characteristic information through a similarity calculation module.

904. And the execution equipment generates second characteristic information corresponding to at least two first characters according to the first similarity information through the generation module.

In the embodiment of the present application, a specific implementation manner of the executing device to execute steps 902 to 904 is similar to a specific implementation manner of steps 302 to 304 in the embodiment corresponding to fig. 3, and reference may be made to the above description, which is not described herein again.

905. The execution device outputs a prediction result corresponding to the character to be predicted through the feature processing network, and the prediction result indicates a predicted character corresponding to the character to be predicted.

In some embodiments of the present application, in the application scenario, the feature processing network may output a prediction result corresponding to the mask character based on the second feature information, so as to obtain a predicted character corresponding to the character to be predicted. Alternatively, the feature processing network may output a prediction result corresponding to the mask character based on the fourth feature information, the prediction result indicating a predicted character corresponding to the character to be predicted. The prediction result may be expressed as an index number corresponding to the predicted character.

In one text prediction process, the number of characters to be predicted may be at least one. When the number of the characters to be predicted is at least two, in the process of text prediction by using the text processing network, an autoregressive prediction method can be adopted, namely, the text processing network can only predict one character every time the text processing network performs prediction operation, the index number of one predicted character is output in the prediction process every time, and all the characters to be predicted can be obtained only by performing the prediction operation at least twice; a non-autoregressive prediction method can also be adopted, namely, every time the text processing network executes a prediction operation, all characters to be predicted can be predicted at one time. As an example, taking a mode of auto regression (autoregressive) as an example here, for example, the text to be processed is "today weather XXX", in this example, a total of 3 predicted characters need to be generated, the execution device may replace the 3 characters to be predicted with [ MASK ] characters, and in the process of generating the first character to be predicted, the execution device inputs "today weather [ MASK ]" into the text processing network, and the text processing network generates a "very" index number. In the process of generating the second character to be predicted, the execution equipment inputs 'today weather is very MASK ] [ MASK ]' into the text processing network, and the text processing network generates a 'not' index number. In the process of generating the third character to be predicted, the execution equipment inputs 'today weather is not so much [ MASK ]' into the text processing network, and the text processing network generates an 'error' index number. Thus, the three predicted characters are determined to be "good" and it should be understood that the examples are only for convenience of understanding and are not intended to limit the present solution.

It should be noted that, in the application scenario, the execution times between steps 902 to 904 and step 905 are not limited, and the execution times between steps 902 to 904 and step 905 may be more than one or one to one, and specifically refer to the description in the embodiment corresponding to fig. 3, which is not described herein again.

In the embodiment of the application, the method and the device can be applied to an application scene of text classification and an application scene of text prediction, and the application scene of the scheme is expanded.

(3) Applied to text translation scene

In the embodiment of the present application, please refer to fig. 10, where fig. 10 is a schematic flowchart of a text processing method provided in the embodiment of the present application, and the text processing method provided in the embodiment of the present application may include:

1001. the execution device enters at least two first characters into the text processing network.

In some embodiments of the present application, the execution device obtains a text to be translated, where the text to be translated includes at least two first characters, and the execution device inputs the entire text to be translated into a text processing network. The specific implementation manner may refer to the description in step 1001, and is not described herein. The text to be translated (i.e., the at least two first characters) is in a first language, the translated text is in a second language, and the first language and the second language are different languages.

The text processing network comprises a feature extraction network and a feature processing network. The feature extraction network comprises a similarity calculation module and a generation module, and the feature extraction network can be specifically expressed as an encoder (encoder); the execution device in the application scenario may be configured with a character table, each character in the character table corresponds to a unique index number, the feature processing network in the application scenario may also be specifically represented as a decoder (decoder), the classifier may output an index number, and the execution device determines the predicted character according to the index number and the character table.

1002. The executing equipment performs feature extraction on the at least two first characters through the similarity calculation module to obtain first feature information corresponding to the at least two first characters.

1003. The executing equipment calculates first similarity information of the at least two first characters at the dimension level according to the first characteristic information through a similarity calculation module.

1004. And the execution equipment generates second characteristic information corresponding to at least two first characters according to the first similarity information through a generation module.

In the embodiment of the present application, a specific implementation manner of the executing device to execute steps 1002 to 1004 is similar to a specific implementation manner of steps 302 to 304 in the embodiment corresponding to fig. 3, and reference may be made to the above description, which is not described herein again.

1005. And the execution equipment executes translation operation on the first character through the feature processing network to obtain a translated first character, wherein the translated first character and the first character are different languages.

In some embodiments of the application, in the application scenario, the execution device may perform a translation operation on the first character in an auto-regressive manner or a non-auto-regressive manner through the feature processing network, output the indication information of the translated first character, and obtain the translated first character according to the indication information of the translated first character. The execution device may further be configured with a character table of the second language, the character table of the second language is configured with a plurality of characters of the second language and an index number corresponding to each character of the second language, the indication information of the translated first character may be specifically expressed as an index number, and the execution device determines the translated first character from the character table of the second language according to the index number included in the indication information of the first character.

Optionally, the feature processing network may perform a translation operation on the first character based on the second feature information and the fourth feature information, and output indication information of the translated first character. The indication information of the translated first character may be specifically expressed as an index number corresponding to the translated first character one to one.

Similar to the embodiment corresponding to fig. 3, the execution times between steps 1002 to 1004 and step 1005 are also not limited in this application scenario, and the execution times between steps 1002 to 1004 and step 1005 may be more than one or one to one, which may specifically refer to the description in the embodiment corresponding to fig. 3, and will not be described herein again.

To further understand the present solution, please refer to fig. 11, and fig. 11 is a schematic structural diagram of a text processing network according to an embodiment of the present application. In FIG. 11, the first language is Chinese and the second language is English. Both the encoder and decoder portions of the machine translation task may be implemented using the feature extraction network multi-layer stack shown in the corresponding embodiment of fig. 3. The input to the encoder is sequence data corresponding to the text to be translated (i.e., sequence data corresponding to the first language), and [ CLS ] is added at the top of the text to be translated, similar to the embodiment described in FIG. 8]Character, using [ CLS]The characters are used for extracting the feature information of the whole text to be translated, and the steps executed by each layer of feature extraction network are similar to those in steps 302 to 304 in the corresponding embodiment of fig. 3. The input of the decoder is the language corresponding to the second language, in each layer except the bottom layer, the matrix a (i.e. the first representation) and the matrix C (i.e. the third representation) of each layer are obtained by linear transformation of the second feature information output by the corresponding layer of the encoder, and the matrix B (i.e. the second representation) is obtained by linear transformation of the second feature information generated by the previous layer in the decoder. Therefore, the characteristic information of the decoder side can be fused with the characteristic information learned by the encoder side, so that the prediction of each word in the decoding process can effectively notice the information of the first language. Decoder in the present embodiment to assume left to rightFor example, after the execution device generates E1 (i.e., the second feature information output by the last feature extraction network) through the whole encoder, the training device replaces the non-generated character with [ MASK ] when generating the first translated character]Inputting characters (as shown in FIG. 11) into the encoder, after obtaining the second feature information output by the last feature extraction network in the encoder, the classification network utilizes h₁To output the indication information of the first translated character, assuming that the obtained first translated character is "It". During the generation of the second translated character, the training device will "[ CLS ]]It[MASK][MASK][MASK][MASK][MASK][MASK][MASK]"input to encoder, Classification network uses the top layer of" It "to represent h₂Generating the indication information of the second translated character, and so on until all the translated characters are obtained, it should be understood that the example in fig. 11 is only for convenience of understanding the scheme and is not used to limit the scheme.

In the embodiment of the application, the method and the device can be applied to the application scenes of text classification and text prediction and the application scenes of text translation, and the application scenes of the scheme are further expanded.

Second, training phase

In the embodiment of the present application, in the scenario that the sequence data is used to indicate character information in the text to be processed, the training phase describes a process of how the training device 220 trains the text processing network. Correspondingly, in the training phase, understanding text information, performing text prediction, text translation, or other specific application scenarios are also included, and the following respectively describes three application scenarios of the foregoing examples.

(1) Applied to the scene of understanding text

In an embodiment of the present application, please refer to fig. 12, where fig. 12 is a schematic flowchart of a training method of a neural network provided in an embodiment of the present application, and the training method of the neural network provided in the embodiment of the present application may include:

1201. the training device obtains a first training text.

In some embodiments of the present application, a first training data set may be configured in the training device, where the first training data set includes a plurality of training texts, each training text includes at least one character to be predicted, and the training data set further includes a correct character corresponding to the mask character. The training device obtains a first training text from the training data set, the first training text includes at least two second characters, the at least two second characters include characters to be predicted, and the characters to be predicted in the training stage can be replaced by mask characters in advance. The MASK characters may be located anywhere in the first training text, such as, for example, "today's [ MASK ] [ MASK ] good" as an example, and "Hua's hands [ MASK ] [ MASK ] good" as another example, and so on, without limitation herein.

1202. The training equipment inputs the first training text into the first feature extraction network, and feature extraction is carried out on the first training text through the first feature extraction network to obtain first feature information corresponding to the at least two second characters.

In the embodiment of the application, after the training device acquires the first training text, the original representation corresponding to the first training text is acquired, and the original representation corresponding to the first training text is input into the first feature extraction network, so that feature extraction is performed on the first training text through the first feature extraction network, and first feature information corresponding to at least two second characters is acquired. For the specific implementation manner of step 1202, refer to the description in step 301 and step 302 in the corresponding embodiment of fig. 3. The difference is that in the training stage, the original representation corresponding to each character in the character table configured on the training device is fixed and invariable, and the original representation corresponding to each character in the character table configured on the training device can also be regarded as a model parameter and is continuously updated in the iterative training process.

1203. The training equipment calculates second similarity information of the at least two second characters in the dimension level according to the first feature information corresponding to the at least two second characters through the first feature extraction network.

1204. And the training equipment generates second feature information corresponding to at least two second characters according to the second similarity information through the first feature extraction network.

1205. The training equipment outputs a prediction result through a first feature processing network, the prediction result indicates a predicted character corresponding to the character to be predicted, and the first feature processing network and the first feature extraction network belong to a first text processing network.

In this embodiment of the application, the specific implementation manner of the training device to execute steps 1203 to 1205 can refer to the description in steps 903 to 905 in the embodiment corresponding to fig. 9, which is not described herein again. The specific expression form of the second similarity information is similar to that described in the embodiment corresponding to fig. 3, and the difference is that the first similarity information is similarity information of at least two first characters at a dimension level, and the second similarity information is similarity information of at least two second characters at a dimension level. The specific expression of the prediction result can be referred to the description of the embodiment corresponding to fig. 9, which is not repeated herein.

It should be noted that if the prediction method of auto-regression is adopted in steps 1203 to 1205, only one predicted character can be obtained in each prediction operation, and if the predicted character is an error character in the whole prediction process, the training device still performs the next prediction by using the correct character in the next prediction process. As an example, for example, the correct prediction result corresponding to the first training text is "weather is good today", while the predicted character obtained in the first prediction process is "null", and it is obvious that a prediction error occurs in the prediction process of this time. If the prediction operation is wrong in the inference stage, the execution equipment inputs 'today' empty [ MASK ] [ MASK ] good 'into the text processing network to perform the next prediction operation, but in the training stage, the training equipment inputs' today 'MASK ] [ MASK ] good' into the text processing network to perform the next prediction operation, namely, even if the prediction is wrong in the training stage, the next prediction can be performed by using correct characters.

1206. The training equipment carries out iterative training on the first text processing network according to the correct character, the predicted character and the loss function corresponding to the character to be predicted until a preset condition is met, and outputs a second feature extraction network, wherein the second feature extraction network is the trained first feature extraction network.

In some embodiments of the present application, after obtaining the prediction result, the training device determines a predicted character corresponding to the character to be predicted, calculates a function value of a first loss function according to a correct character, the predicted character, and the first loss function corresponding to the character to be predicted, where the first loss function reflects a similarity between the correct character and the predicted character, generates a gradient value according to the function value of the first loss function, and performs a gradient update on weight parameters of each neural network layer in the first text processing network through a back propagation algorithm (back propagation), and optionally, may also update an original representation corresponding to the first training text, so as to complete one training on the first text processing network. And the training equipment repeatedly executes the steps 1201 to 1206 to carry out iterative training on the first text processing network until a preset condition is met, and outputs a second feature extraction network, wherein the second feature extraction network is the trained first feature extraction network.

The preset condition may be a convergence condition that satisfies the first loss function, or may be that the iteration number of the iterative training satisfies a preset number, and the like. The first loss function may be embodied as a 0-1 loss function, a cross-entropy loss function, a logarithmic loss function, or other types of loss functions, and the like, which are not limited herein. To further understand the first loss function, the following formula of the first loss function is disclosed as an example in which a non-autoregressive prediction method is adopted and the first loss function is a logarithmic loss function:

L(θ₁)＝logΠ_i∈Sp(y_i|x；θ₁)＝∑_i∈Slogp(y_i|x；θ₁)； (1)

wherein, L (theta)₁) Representing the first loss function, θ₁Representing a weight parameter, optionally theta, in the first text-processing network₁The method further comprises an original representation corresponding to the first training text, x represents the input first training text, S represents a collection of positions of all characters to be predicted in the first training text, y_iIndicating that the position at i is to be predictedMeasuring characters, it should be understood that the example in the formula (1) is only for convenience of understanding the first loss function, and is not used to limit the present solution.

1207. The training device obtains a second training text.

In some embodiments of the present application, a second training set may be further configured in the training device, where the second training data set includes a plurality of training texts and a correct category corresponding to each training text. The training device obtains a second training text from the second training data set, wherein the second training text comprises at least two third characters.

1208. The training device inputs a second training text into the second feature extraction network to generate second feature information corresponding to the at least two third characters through the second feature extraction network.

1209. And the training equipment executes classification operation through a second feature processing network and outputs indication information of prediction categories corresponding to at least two third characters, wherein the second feature extraction network and the second feature processing network belong to a second text processing network.

In this embodiment of the application, the specific implementation manner of the training device to execute steps 1203 to 1205 can refer to the description in steps 301 to 305 in the corresponding embodiment of fig. 3, which is not described herein again. The indication information of the prediction categories corresponding to the at least two third characters and the concrete representation forms of the prediction categories can refer to the description in the embodiment corresponding to fig. 3, and are not described herein again.

1210. And the training equipment carries out iterative training on the second text processing network according to the correct category corresponding to the second training text, the indication information of the prediction category and the loss function until preset conditions are met, and outputs the trained second text processing network.

In some embodiments of the present application, after obtaining the indication information of the prediction categories corresponding to the at least two third characters, the training device determines the prediction category corresponding to the second training text, and calculates the function value of the second loss function according to the correct category corresponding to the second training text, the prediction category, and the second loss function. And the second loss function reflects the similarity between the correct category and the prediction category, and the concrete representation form of the correct category is consistent with that of the prediction category. The training device generates a gradient value according to the function value of the second loss function, and updates the weight parameters of each neural network layer in the second text processing network in a gradient manner through a back propagation algorithm, optionally, the original representation corresponding to the second training text can be updated, so as to complete one-time training of the second text processing network. And the training device repeatedly executes the steps 1201 to 1206 to perform iterative training on the second text processing network until a preset condition is met, and outputs the trained second text processing network.

The preset condition may be a convergence condition that satisfies the second loss function, or may be that the iteration number of the iterative training satisfies a preset number, and the like. The second loss function may be embodied as a 0-1 loss function, a cross-entropy loss function, a logarithmic loss function, or other types of loss functions, and the like, which are not limited herein. To further understand the first loss function, the formula of the second loss function is disclosed as follows, taking the second loss function as a logarithmic loss function as an example:

L(θ₂)＝logP(y|x；θ₂)； (2)

wherein, represents the second loss function, θ₂Representing a weight parameter, optionally theta, in the second text-processing network₂And an original representation corresponding to the second training text is further included, x represents the input second training text, and y represents a prediction classification and a correct classification corresponding to the second training text, and it should be understood that the example in the formula (2) is only for convenience of understanding the first loss function, and is not used for limiting the scheme.

It should be noted that steps 1207 to 1210 are optional steps, and if steps 1207 to 1210 are not executed, the second feature extraction network may be directly output after step 1206 is executed. If steps 1207 to 1210 are executed, steps 1201 to 1206 may be regarded as a pre-training (pre-training) stage, steps 1201 to 1206 may adopt a learning manner that may be self-supervision, and steps 1207 to 1210 may be regarded as a fine tuning (tuning) stage.

In the embodiment of the application, the feature extraction network in the text classification network is trained, then the whole text classification network is trained, semantic information learned in the primary training process can be effectively transferred to the whole text classification network, and the accuracy of the text classification network after training is improved.

In the embodiment of the application, the first feature extraction network is placed in a scene of text prediction, and the first feature extraction network is iteratively trained, so that the requirement on the capability of the feature extraction network in the scene of text prediction is high, and the feature extraction capability of the trained first feature extraction network is favorably improved.

(2) Application in scenes of text prediction

In an embodiment of the present application, please refer to fig. 13, where fig. 13 is a schematic flowchart of a training method of a neural network provided in an embodiment of the present application, and the training method of the neural network provided in the embodiment of the present application may include:

1301. the training equipment acquires a first training text, wherein the first training text comprises at least two second characters, and the at least two second characters comprise at least one character to be predicted.

In the embodiment of the present application, a specific implementation manner of the training device to execute step 1301 may refer to description in step 1101 in the embodiment corresponding to fig. 11, which is not described herein again.

1302. The training equipment inputs at least two second characters into a first feature extraction network, feature extraction is carried out on the at least two second characters through the first feature extraction network, first feature information corresponding to the at least two second characters is obtained, and the first feature information comprises feature information of the at least two second characters in at least one dimension.

1303. The training equipment calculates second similarity information of the at least two second characters in the dimension level according to the first feature information corresponding to the at least two second characters through the first feature extraction network.

1304. And the training equipment generates second feature information corresponding to at least two second characters according to the second similarity information through the first feature extraction network.

1305. And the training equipment outputs a predicted character corresponding to the character to be predicted through the first feature processing network.

In this embodiment of the application, the specific implementation manner of the training device executing steps 1302 to 1305 may refer to the descriptions in steps 901 to 905 in the embodiment corresponding to fig. 9, which is not described herein again. In this embodiment, the first feature extraction network and the first feature processing network are included in the same text prediction network, and the specific implementation manner of the text prediction network may refer to the description of the first text processing network in the embodiment corresponding to fig. 9, and the specific representation form of the first feature information corresponding to the at least two second characters, the specific representation form of the second similarity information, and the specific representation form of the second feature information corresponding to the at least two second characters are all described in the embodiment corresponding to fig. 3, and may refer to the above description, which is not repeated herein.

1306. And the training equipment carries out iterative training on the text prediction network according to the correct character corresponding to the character to be predicted, the predicted character and the third loss function until a preset condition is met, and outputs the trained text prediction network.

In this embodiment of the application, a specific implementation manner of the training device to execute step 1306 may refer to description in step 1106 in the embodiment corresponding to fig. 11, which is not described herein again. The preset condition may be a convergence condition that satisfies the third loss function, or may be that the iteration number of the iterative training satisfies a preset number, and the like. The specific expression of the third loss function may be similar to that of the first loss function, and is not described herein.

In the embodiment of the application, a training method when a text processing network is used for text prediction is provided, and the application scene of the scheme is expanded.

(3) Applied to text translation scene

In an embodiment of the present application, please refer to fig. 14, where fig. 14 is a schematic flowchart of a training method of a neural network provided in an embodiment of the present application, and the training method of the neural network provided in the embodiment of the present application may include:

1401. the training equipment acquires a first training text and a correct translation text corresponding to the first training text, wherein the first training text comprises at least two second characters.

1402. The training equipment inputs the first training text into a fourth feature extraction network, feature extraction is carried out on the first training text through the fourth feature extraction network, first feature information corresponding to at least two second characters is obtained, and the first feature information comprises feature information of the second characters in at least one dimension.

1403. And the training equipment calculates second similarity information of the at least two second characters at the dimension level according to the first feature information corresponding to the at least two second characters through a fourth feature extraction network.

1404. And the training equipment generates second feature information corresponding to at least two second characters according to the second similarity information through a fourth feature extraction network.

In the embodiment of the present application, the specific implementation manner of the training device to execute steps 1402 to 1404 may refer to the description in steps 1002 to 1004 in the corresponding embodiment of fig. 10, which is not described herein again.

1405. The training equipment executes translation operation on the first training text through a fourth feature processing network according to the correctly translated text and the second feature information, and outputs a translated second character, the fourth feature extraction network and the fourth feature processing network belong to the text translation network, and the translated second character is in a different language from the second character.

In the embodiment of the present application, the specific implementation manner of the training apparatus executing step 1405 is similar to the specific implementation manner of step 1005 in the embodiment corresponding to fig. 10. The difference is that if the auto-regressive translation method is used in steps 1402 to 1405, only one translated character can be obtained in each translation operation, and if a certain translated character is a wrong character in the whole translation process, the training device still performs the next translation with the correct translated character in the next translation process. As an example, for example, the correct translation character corresponding to the first character of the first training text "weather good today" is "It", and the translation character obtained in the first translation process is "That", and It is obvious That a translation error occurs in the translation process of this time. If in the inference phase, the execution device will input the "That [ MASK ]" to the encoder for the next translation operation, but in the training phase, the training device will input the "It [ MASK ]" to the encoder for the next translation operation, That is, even if a translation error occurs in the training phase, the correct translated character will still be used for the next translation.

1406. And the training equipment carries out iterative training on the text translation network according to the correctly translated text, the translated second character and the fourth loss function until a preset condition is met, and outputs the trained text translation network.

In the embodiment of the present application, reference may be made to the description of step 1206 in the corresponding embodiment in fig. 12. The difference is that the fourth loss function indicates the similarity between the correctly translated text and the translated second character. The preset condition may be a convergence condition that satisfies the fifth loss function, or may be that the iteration number of the iterative training satisfies a preset number, and the like. The fifth loss function may be embodied as a 0-1 loss function, a cross-entropy loss function, a logarithmic loss function, or other types of loss functions, and the like, which are not limited herein.

In the embodiment of the application, a training method when a text processing network is used for text translation is provided, and the application scenario of the scheme is further expanded.

In the embodiment of the application, the trained first text processing network can process two or more characters at one time, namely, a more efficient text processing network is provided; in addition, in the process of generating the feature information of the characters, the trained first text processing network can combine the similarity information of the characters in the dimension level, that is, the generated second feature information can be fused with information of finer granularity, which is beneficial to improving the precision of the whole text processing network.

In the above description, the method for processing sequence data provided in the embodiment of the present application is introduced in a large application scenario of text processing, and the embodiment of the present application further provides a method for processing sequence data, please refer to fig. 15, where fig. 15 is a schematic flow diagram of the method for processing sequence data provided in the embodiment of the present application, and the method for processing sequence data provided in the embodiment of the present application may include:

1501. the execution device inputs at least two sequence data into a first neural network, which is a processing network of the sequence data.

In the embodiment of the present application, the sequence data is used to indicate any one of the following information: character information, consumption information, location information, and genetic information. The first neural network includes a feature extraction network and a feature processing network, the feature extraction network also includes a similarity calculation module and a generation module, and specific implementation manners of the similarity calculation module, the generation module, and the feature processing network may be determined by combining specific functions of the first neural network, and in the embodiments corresponding to fig. 3, 9, and 10, specific implementation manners of the similarity calculation module, the generation module, and the feature processing network under specific application scenarios are described, and are not described herein again.

Specifically, when the sequence data is used to indicate the character information, the specific implementation manner of step 1501 has been described in detail in the embodiments corresponding to fig. 3 to 14, and is not described herein again.

When the sequence data is used to indicate historical consumption information of the user, the first neural network may function to predict future consumption by the user. Step 1501 may include: the execution device converts the historical consumption information of the user into at least two sequence data, and inputs the at least two sequence data into the first neural network, wherein one sequence data corresponds to one consumption record. The original representation of each consumption record (i.e., a sequence of data corresponding to each consumption record) can be obtained by, but is not limited to, random initialization or by using Item embedding (Item2Vec), which can generate a corresponding vector representation (i.e., a sequence of data) for each application. By way of example, an application that a user has downloaded may be converted into an expression of sequence data, one sequence data for each consumption record, at least two sequence data indicating a sequence of applications that the user has downloaded, such as: "application 1, application 2, application 3, application 4, and application 5", the information output by the first neural network indicating applications that may be of interest to the user includes: "application 6, application 7, application 8, application 9, and application 10".

When the sequence data is used for indicating the position information of the user, the first neural network can be used for predicting the position information of the user, and the application is widely applied to scenes of people flow prediction, information pushing based on geographic positions and the like. Step 1501 may include: the execution device converts the historical location information of the user into at least two pieces of sequence data, and inputs the at least two pieces of sequence data into the first neural network, one piece of sequence data corresponding to one location record, and the at least two pieces of sequence data indicate all historical locations of the user. The initial representation of each piece of location information (i.e., one piece of sequence data corresponding to each piece of location information) may be obtained by, but is not limited to, random initialization or using location embedding (Loc2Vec), which may generate a corresponding vector representation (i.e., one piece of sequence data) for each piece of location information. By way of example, locations that a user has already visited include, for example: "home, restaurant, company, restaurant and company", the information output by the first neural network indicating a likely future location of the user includes: "mall, park, company, mall, and home".

When the sequence data is used to indicate genetic information of the user, the first neural network may function as a task of performing sequence labeling on the gene sequences, that is, classifying each gene in the gene sequences. Step 1501 may include: the execution device converts at least two gene elements included in the gene sequence into at least two sequence data, one sequence data corresponding to one gene element, and inputs the at least two sequence data into the first neural network, the at least two sequence data indicating all the gene elements in the gene sequence. The initial representation of each genetic element (i.e., one sequence data corresponding to each genetic element) may be obtained by, but is not limited to, random initialization. For example, the gene sequence is taken as an example as a base sequence, for example, the base sequence input to the first neural network is { a, T, G, C, T, a }, the tag sequence output by the first neural network is {0, 0, 1, 0, 0, 0, 0, 1}, 1 in the tag sequence refers to a gene element in which the corresponding gene element is valuable, for example, a gene element at which translation starts, and 0 in the tag sequence refers to a gene element in which the corresponding gene element is not valuable.

It should be noted that other types of data can also be indicated by the sequence data, so that the processing method of the sequence data provided in the embodiment of the present application is used for processing, and the information that the sequence data can indicate is not exhaustive here.

1502. And the executing equipment performs feature extraction on the at least two sequence data through the similarity calculation module to obtain third feature information corresponding to the at least two sequence data.

1503. And the executing equipment calculates third similarity information of the at least two sequence data at the dimension level according to the third characteristic information through a similarity calculation module.

1504. The execution device generates fourth feature information corresponding to the at least two sequence data according to the third similarity information.

In this embodiment of the application, the specific implementation manner of the executing device executing steps 1502 to 1504 may refer to the description in steps 302 to 304 in the corresponding embodiment of fig. 3, which is not described herein again. The meaning of the third feature information is similar to the first feature information in the embodiment corresponding to fig. 3 to 11, the meaning of the third similarity information is similar to the first similarity information in the embodiment corresponding to fig. 3 to 11, and the meaning of the fourth feature information is similar to the second feature information in the embodiment corresponding to fig. 3 to 11, which are not repeated herein.

1505. The execution device performs feature processing through a feature processing network and outputs a generation processing result corresponding to at least two sequence data.

In the embodiment of the application, the execution device performs feature processing through a feature processing network and outputs a generation processing result corresponding to at least two sequence data, wherein the feature processing is any one of the following operations: classification operations, prediction operations, and translation operations. Specifically, step 305 in the embodiment corresponding to fig. 3, step 905 in the embodiment corresponding to fig. 9, and step 1005 in the embodiment corresponding to fig. 10 are all a specific implementation manner of step 1505, and refer to the description.

Besides, in the case that the sequence data is used to indicate historical consumption information of the user or historical location information of the user, and the function of the first neural network is to predict future consumption information of the user or future location information of the user, a specific implementation manner of step 1505 may refer to the description of step 905 in the corresponding embodiment of fig. 9, and similar to the corresponding embodiment of fig. 9, the execution device may also replace consumption information or location information to be predicted with a mask sequence, so as to perform a prediction operation according to at least two known sequence data to predict the mask sequence, that is, to generate a predicted consumption record according to at least two known consumption records, or to generate predicted location information according to at least two known location information.

In the case that the sequence data is used to indicate gene information, and the first neural network functions as a sequence tag for the gene sequence, the specific implementation manner of step 1505 can refer to the description of step 305 in the corresponding embodiment of fig. 3, and similar to the corresponding embodiment of fig. 3, the executing device can perform a classifying operation by using a classifying network (i.e., an example of a feature processing network) to output a class tag and the like corresponding to each gene element. The specific case where the sequence data indicates other types of data is not exhaustive.

In the embodiment of the application, not only can the character information be processed, but also the sequence information such as consumption information, position information or gene information can be processed, the application scene of the scheme is further expanded, and the realization flexibility of the scheme is improved.

Referring to fig. 16, fig. 16 is a schematic flow chart of a training method of a neural network provided in an embodiment of the present application, where the training method of a neural network provided in an embodiment of the present application may include:

1601. the training equipment acquires a second training sample and a correct processing result corresponding to the second training sample, wherein the second training sample comprises at least two training sequence data.

In the embodiment of the present application, a specific implementation manner of step 1601 may refer to descriptions in step 1201 and step 1207 in the embodiment corresponding to fig. 12, and step 1301 in the embodiment corresponding to fig. 13, and step 1401 in the embodiment corresponding to fig. 14. Wherein, the concept of the at least two training sequence data is similar to that of the at least two training sequence data in the corresponding embodiment of fig. 15, and is not repeated here.

1602. The training equipment inputs at least two training sequence data into a first neural network, feature extraction is carried out on the at least two training sequence data through a similarity calculation module, third feature information corresponding to the at least two training sequence data is obtained, and the first neural network is a processing network of the sequence data.

1603. And the training equipment calculates third similarity information of the at least two training sequence data at the dimension level according to the third characteristic information through a similarity calculation module.

1604. And the training equipment generates fourth characteristic information corresponding to at least two training sequence data according to the third similarity information.

1605. The training equipment performs characteristic processing through a characteristic processing network and outputs a generation processing result corresponding to at least two training sequence data.

In the embodiment of the present application, the specific implementation manner of the training device to execute steps 1602 to 1605 may refer to the description in steps 1502 to 1506 in the embodiment corresponding to fig. 15, which is not described herein again.

1606. And the training equipment carries out iterative training on the first neural network according to the generated processing result, the accurate processing result and the loss function until a preset condition is met, and outputs the trained first neural network.

In this embodiment of the application, a specific implementation manner of step 1606 may refer to descriptions in step 1206 and step 1210 in the embodiment corresponding to fig. 12, step 1306 in the embodiment corresponding to fig. 13, and step 1406 in the embodiment corresponding to fig. 14. For a specific concept of the generation processing result and the accurate processing result corresponding to the at least two training sequence data, reference may be made to the description of the concept of the generation processing result corresponding to the at least two training sequence data in the embodiment corresponding to fig. 15, which is not described herein again.

On the basis of the embodiments corresponding to fig. 1 to 16, in order to better implement the above-mentioned scheme of the embodiments of the present application, the following also provides related equipment for implementing the above-mentioned scheme. Specifically referring to fig. 17, fig. 17 is a schematic structural diagram of a text processing network according to an embodiment of the present application. The text processing network 1700 may include a feature extraction network 1710 that includes a similarity calculation module 1711 and a generation module 1712. The similarity calculation module 1711 is configured to receive at least two input first characters, perform feature extraction on the at least two first characters, and obtain first feature information corresponding to the at least two first characters, where the first feature information includes feature information of the first characters in at least one dimension; the similarity calculation module 1711 is further configured to calculate, according to the first feature information, first similarity information of the at least two first characters at a dimension level; a generating module 1712, configured to generate second feature information corresponding to the at least two first characters according to the first similarity information.

In one possible design, the first feature information includes a first representation and a second representation, the first representation and the second representation are both matrixes, a column of data in the first representation includes feature information of at least two first characters in one dimension, and a column of data in the second representation includes feature information of at least two first characters in one dimension;

the similarity calculation module 1711 is specifically configured to calculate a similarity between the column data in the first representation and the column data in the second representation, so as to obtain first similarity information.

In one possible design, the similarity calculation module 1711 is specifically configured to transpose the first representation, and calculate a similarity between the row data in the transposed first representation and the column data in the second representation, so as to obtain the first similarity information.

In one possible design, the similarity calculation module 1711 is specifically configured to: performing dot product on the column data in the first representation and the column data in the second representation to generate similarity; or, calculating a euclidean distance between the column data in the first representation and the column data in the second representation to generate a similarity; or, calculating a manhattan distance between the column data in the first representation and the column data in the second representation to generate a similarity; or, calculating a mahalanobis distance between the column data in the first representation and the column data in the second representation to generate a similarity; alternatively, a cosine similarity between the column data in the first representation and the column data in the second representation is calculated to generate the similarity.

In one possible design, the similarity calculation module 1711 is specifically configured to perform a linear transformation on the original representations corresponding to the at least two first characters to obtain a first representation and a second representation.

In one possible design, the similarity calculation module 1711 is specifically configured to multiply the original representations of the at least two first characters by a first linear transformation matrix to obtain a first representation, and multiply the original representations of the at least two first characters by a second linear transformation matrix to obtain a second representation.

In one possible design, the similarity calculation module 1711 is specifically configured to perform convolution processing on the original representations corresponding to the at least two first characters to obtain a first representation and a second representation.

In one possible design, the first feature information includes a third representation that includes feature information of at least two first characters; the generating module 1712 is specifically configured to perform fusion processing on the third representation and the first similarity information to generate second feature information.

In one possible design, the number of the first characters is N, where N is an integer greater than or equal to 2, and the generating module 1712 is specifically configured to: generating a third-order tensor representation according to the third representation and the first similarity information, wherein the third-order tensor representation comprises N matrixes, each matrix corresponds to one first character, and the feature information of one first character and the similarity information of one first character in the dimension level are fused in one matrix; and compressing the third-order tensor expression to obtain second characteristic information.

In one possible design, the first similarity information and the third representation are both matrices; a generating module 1712, specifically configured to perform tensor product operation on the column data in the first similarity information and the column data represented by the third representation to obtain a third-order tensor representation; or, the generating module 1712 is specifically configured to perform addition operation on the column data in the first similarity information and the column data in the third representation, so as to obtain a third-order tensor representation.

In one possible design, the third order tensor representation includes N matrices, each of which is a two-dimensional matrix of d × d; the generating module 1712 is specifically configured to perform compression processing on the third-order tensor expression along one d direction of the third-order tensor expression to obtain second feature information, where the second feature information is an N × d two-dimensional matrix, and a compression processing mode includes any one of the following: convolution, addition, averaging, taking the maximum or taking the minimum.

In a possible design, the generating module 1712 is specifically configured to perform transposition processing on the third representation, and multiply the transposed third representation by the first similarity information to obtain the second feature information.

In one possible design, the text processing network 1700 further includes a feature processing network 1720, the feature processing network 1720 configured to perform a classification operation based on the second feature information and output information indicative of a predicted class corresponding to at least two first characters, wherein the classification of the class is based on semantics of the characters or a part of speech of the characters.

In one possible design, the at least two first characters include a character to be predicted, the text processing network further includes a feature processing network 1720, and the feature processing network 1720 is configured to output a prediction result corresponding to the character to be predicted based on the second feature information, and the prediction result indicates a predicted character corresponding to the character to be predicted.

In one possible design, the text processing network further includes a feature processing network 1720, where the feature processing network 1720 is configured to perform a translation operation on the first character based on the second feature information to obtain a translated first character, and the translated first character is in a different language from the first character.

It should be noted that, the information interaction, the execution process, and other contents between the modules/units in the text processing network 1700 are based on the same concept as the method embodiments corresponding to fig. 3 to fig. 11 in the present application, and specific contents may refer to the description in the foregoing method embodiments in the present application, and are not described herein again.

Fig. 18 is a schematic structural diagram of a training apparatus for a neural network according to an embodiment of the present invention, where the training apparatus 1800 for a neural network includes: an acquisition module 1801, an input module 1802, a processing module 1803, and an output module 1804. The obtaining module 1801 is configured to obtain a first training text, where the first training text includes at least two second characters; an input module 1802, configured to input the first training text into a first feature extraction network, so as to perform feature extraction on the first training text through the first feature extraction network, to obtain first feature information corresponding to at least two second characters, where the first feature information includes feature information of the second characters in at least one dimension; a processing module 1803, configured to calculate, through a first feature extraction network, second similarity information of at least two second characters at a dimension level according to first feature information corresponding to the at least two second characters; the processing module 1803 is further configured to generate, through the first feature extraction network, second feature information corresponding to at least two second characters according to the second similarity information; an output module 1804, configured to output, through a first feature processing network, a generation processing result corresponding to the at least two second characters based on second feature information corresponding to the at least two second characters, where the first feature processing network and the first feature extraction network belong to a first text processing network; the processing module 1803 is further configured to perform iterative training on the first text processing network according to the correct processing result corresponding to the at least two second characters, the generated processing result, and the loss function until a preset condition is met.

In one possible design, the at least two second characters include at least one character to be predicted; an output module 1804, specifically configured to output, through the first feature processing network, a prediction result corresponding to the character to be predicted based on the second feature information corresponding to the at least two second characters, where the prediction result indicates a predicted character corresponding to the character to be predicted; the processing module 1803 is specifically configured to perform iterative training on the first text processing network according to the correct character, the predicted character, and the loss function corresponding to the character to be predicted, until a preset condition is met, and output a second feature extraction network, where the second feature extraction network is the trained first feature extraction network.

In a possible design, the obtaining module 1801 is further configured to obtain a second training text, where the second training text includes at least two third characters; the input module 1802 is further configured to input a second training text into a second feature extraction network, so as to generate second feature information corresponding to at least two third characters through the second feature extraction network; the output module 1804 is further configured to perform a classifying operation based on second feature information corresponding to the at least two third characters through a second feature processing network, and output indication information of prediction categories corresponding to the at least two third characters, wherein the second feature extraction network and the second feature processing network belong to the second text processing network; the processing module 1803 is specifically configured to perform iterative training on the second text processing network according to the correct category corresponding to the second training text, the indication information of the prediction category, and the loss function until a preset condition is met, and output the trained second text processing network.

In one possible design, the at least two second characters include at least one character to be predicted; an output module 1804, specifically configured to output, through the first feature processing network, a prediction result corresponding to the character to be predicted based on the second feature information corresponding to the at least two second characters and the auto-regression algorithm, where the prediction result indicates a predicted character corresponding to the character to be predicted; the processing module 1803 is specifically configured to perform iterative training on the first text processing network according to the correct character, the predicted character, and the loss function corresponding to the character to be predicted, until a preset condition is met, and output the trained first text processing network.

In one possible design, the output module 1804 is specifically configured to perform, according to a correctly translated text corresponding to a first training text and second feature information corresponding to at least two second characters, a translation operation on the first training text through a first feature processing network, and output a translated second character, where the translated second character is in a different language from the second character; the processing module 1803 is specifically configured to perform iterative training on the first text processing network according to the correctly translated text, the translated second character, and the loss function until a preset condition is met, and output the trained first text processing network.

It should be noted that, the information interaction, the execution process, and the like between the modules/units in the training apparatus 1800 of the neural network are based on the same concept as the method embodiments corresponding to fig. 12 to fig. 14 in the present application, and specific contents may refer to the description in the foregoing method embodiments in the present application, and are not repeated herein.

Referring to fig. 19, fig. 19 is a schematic structural diagram of an execution device provided in the embodiment of the present application, where the execution device 1900 may be deployed with the text processing network 1700 described in the embodiment corresponding to fig. 17, so as to implement the functions of the execution device in the embodiments corresponding to fig. 3 to fig. 11. Alternatively, the execution device 1900 is used to implement the functions of the execution device in the corresponding embodiment of fig. 15. Specifically, the execution device 1900 includes: a receiver 1901, a transmitter 1902, a processor 1903 and a memory 1904 (wherein the number of processors 1903 in the execution device 1900 may be one or more, and one processor is taken as an example in fig. 19), wherein the processor 1903 may include an application processor 19031 and a communication processor 19032. In some embodiments of the present application, the receiver 1901, the transmitter 1902, the processor 1903, and the memory 1904 may be connected by a bus or other means.

The memory 1904 may include both read-only memory and random access memory, and provides instructions and data to the processor 1903. A portion of the memory 1904 may also include non-volatile random access memory (NVRAM). The memory 1904 stores processors and operating instructions, executable modules or data structures, or subsets thereof, or expanded sets thereof, wherein the operating instructions may include various operating instructions for performing various operations.

The processor 1903 controls the operation of the execution apparatus. In a particular application, the various components of the execution device are coupled together by a bus system that may include a power bus, a control bus, a status signal bus, etc., in addition to a data bus. For clarity of illustration, the various buses are referred to in the figures as a bus system.

The method disclosed in the above embodiments of the present application may be applied to the processor 1903, or implemented by the processor 1903. The processor 1903 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 1903. The processor 1903 may be a general-purpose processor, a Digital Signal Processor (DSP), a microprocessor or a microcontroller, and may further include an Application Specific Integrated Circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The processor 1903 may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 1904, and the processor 1903 reads the information in the memory 1904 and completes the steps of the method in combination with the hardware.

The receiver 1901 may be used to receive input numeric or character information and generate signal inputs related to performing device related settings and function control. The transmitter 1902 may be configured to output numeric or character information through a first interface; the transmitter 1902 may also be configured to send instructions to the disk groups via the first interface to modify data in the disk groups; the emitter 1902 may also include a display device such as a display screen.

In this embodiment, in one case, the application processor 19031 is configured to execute the functions of the execution device in the corresponding embodiments of fig. 3 to fig. 11. It should be noted that, for specific implementation manners and advantageous effects brought by the application processor 19031 for executing the functions of the execution device in the embodiments corresponding to fig. 3 to fig. 11, reference may be made to descriptions in each method embodiment corresponding to fig. 3 to fig. 11, and details are not repeated here.

In this embodiment, in another case, the application processor 19031 is configured to execute the functions of the execution device in the embodiment corresponding to fig. 15. It should be noted that, for specific implementation manners and advantageous effects brought by the application processor 19031 for executing the functions of the execution device in the embodiment corresponding to fig. 15, reference may be made to descriptions in each method embodiment corresponding to fig. 15, and details are not repeated here.

Referring to fig. 20, fig. 20 is a schematic structural diagram of a training device provided in the embodiment of the present application, and a training device 1800 described in the embodiment corresponding to fig. 18 may be disposed on a training device 2000 to implement the functions of the training device corresponding to fig. 12 to 14; alternatively, the training apparatus 2000 is used to implement the functionality of the training apparatus corresponding to fig. 16. In particular, training apparatus 2000 is implemented by one or more servers, and training apparatus 2000 may have relatively large differences due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 2022 (e.g., one or more processors) and memory 2032, one or more storage media 2030 (e.g., one or more mass storage devices) storing applications 2042 or data 2044. The memory 2032 and the storage medium 2030 may be, among other things, transient storage or persistent storage. The program stored on storage medium 2030 may include one or more modules (not shown), each of which may include a sequence of instructions for operating on the exercise device. Still further, central processor 2022 may be disposed in communication with storage medium 2030, and executes a sequence of instruction operations on training device 2000 from storage medium 2030.

Training apparatus 2000 may also include one or more power supplies 2026, one or more wired or wireless network interfaces 2050, one or more input/output interfaces 2058, and/or one or more operating systems 2041, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

In this embodiment, in one case, the central processing unit 2022 is used to implement the functions of the training apparatus in the embodiment corresponding to fig. 12 to 14. It should be noted that, for the specific implementation manner and the advantageous effects brought by the central processing unit 2022 executing the functions of the training device in the embodiments corresponding to fig. 12 to fig. 14, reference may be made to the descriptions in each method embodiment corresponding to fig. 12 to fig. 14, and details are not repeated here.

In this embodiment, in another case, the central processing unit 2022 is used to implement the function of the training apparatus in the embodiment corresponding to fig. 16. It should be noted that, for the specific implementation manner and the beneficial effects brought by the central processing unit 2022 executing the functions of the training device in the embodiment corresponding to fig. 16, reference may be made to the description in each method embodiment corresponding to fig. 16, and details are not repeated here.

An embodiment of the present application further provides a computer-readable storage medium, which stores a program, and when the program runs on a computer, the program causes the computer to execute the steps performed by the apparatus in the embodiment corresponding to fig. 3 to 11, or execute the steps performed by the training apparatus in the embodiment corresponding to fig. 12 to 14, or execute the steps performed by the apparatus in the embodiment corresponding to fig. 15, or execute the steps performed by the training apparatus in the embodiment corresponding to fig. 16.

Embodiments of the present application also provide a computer program product, which when executed on a computer causes the computer to perform the steps performed by the training apparatus in the embodiment corresponding to fig. 3 to 11, or the steps performed by the training apparatus in the embodiment corresponding to fig. 12 to 14, or the steps performed by the training apparatus in the embodiment corresponding to fig. 15, or the steps performed by the training apparatus in the embodiment corresponding to fig. 16.

Further provided in an embodiment of the present application is a circuit system, including a processing circuit, configured to perform the steps performed by the apparatus as described in the embodiment corresponding to fig. 3 to 11, or perform the steps performed by the training apparatus as described in the embodiment corresponding to fig. 12 to 14, or perform the steps performed by the apparatus as described in the embodiment corresponding to fig. 15, or perform the steps performed by the training apparatus as described in the embodiment corresponding to fig. 16.

The execution device or the training device provided by the embodiment of the present application may specifically be a chip, where the chip includes: a processing unit, which may be for example a processor, and a communication unit, which may be for example an input/output interface, a pin or a circuit, etc. The processing unit may execute computer-executable instructions stored in the storage unit to enable the chip to perform the steps performed by the training apparatus in the embodiment corresponding to fig. 3 to 11, or perform the steps performed by the training apparatus in the embodiment corresponding to fig. 12 to 14, or perform the steps performed by the training apparatus in the embodiment corresponding to fig. 15, or perform the steps performed by the training apparatus in the embodiment corresponding to fig. 16. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, and the like, and the storage unit may also be a storage unit located outside the chip in the wireless access device, such as a read-only memory (ROM) or another type of static storage device that can store static information and instructions, a Random Access Memory (RAM), and the like.

Specifically, referring to fig. 21, fig. 21 is a schematic structural diagram of a chip provided in the embodiment of the present application, where the chip may be represented as a neural network processor NPU 210, and the NPU 210 is mounted on a main CPU (Host CPU) as a coprocessor, and the Host CPU allocates tasks. The core portion of the NPU is an arithmetic circuit 2103, and the controller 2104 controls the arithmetic circuit 2103 to extract matrix data in the memory and perform multiplication.

In some implementations, the arithmetic circuit 2103 internally includes a plurality of processing units (PEs). In some implementations, the operational circuit 2103 is a two-dimensional systolic array. The operational circuit 2103 may also be a one-dimensional systolic array or other electronic circuit capable of performing mathematical operations such as multiplication and addition. In some implementations, the arithmetic circuitry 2103 is a general-purpose matrix processor.

For example, assume that there is an input matrix A, a weight matrix B, and an output matrix C. The arithmetic circuit 2103 fetches the data corresponding to the matrix B from the weight memory 2102 and buffers the data in each PE in the arithmetic circuit. The arithmetic circuit 2103 takes the matrix a data from the input memory 2101 and performs matrix arithmetic on the matrix B, and a partial result or a final result of the obtained matrix is stored in an accumulator 2108.

The unified memory 2106 stores input data and output data. The weight data directly passes through a Memory Access Controller (DMAC) 2105, and the DMAC is transferred to the weight Memory 2102. The input data is also carried into the unified memory 2106 by the DMAC.

The BIU is a Bus Interface Unit 2110 for the interaction of the AXI Bus with the DMAC and an Instruction Fetch memory (IFB) 2109.

The Bus Interface Unit 2110(Bus Interface Unit, BIU for short) is configured to obtain an instruction from the external memory by the instruction fetch memory 2109, and is further configured to obtain original data of the input matrix a or the weight matrix B from the external memory by the storage Unit access controller 2105.

The DMAC is mainly used to transfer input data in the external memory DDR to the unified memory 2106, or to transfer weight data into the weight memory 2102, or to transfer input data into the input memory 2101.

The vector calculation unit 2107 includes a plurality of operation processing units, and performs further processing such as vector multiplication, vector addition, exponential operation, logarithmic operation, magnitude comparison, and the like on the output of the operation circuit 2103 if necessary. The method is mainly used for non-convolution/full-connection layer network calculation in the neural network, such as Batch Normalization, pixel-level summation, up-sampling of a feature plane and the like.

In some implementations, the vector calculation unit 2107 can store the processed output vector to the unified memory 2106. For example, the vector calculation unit 2107 may apply a linear function and/or a nonlinear function to the output of the operation circuit 2103, such as linear interpolation of the feature planes extracted by the convolution layer, and further such as a vector of accumulated values to generate the activation value. In some implementations, the vector calculation unit 2107 generates normalized values, pixel-level summed values, or both. In some implementations, the vector of processed outputs can be used as activation inputs to the operational circuitry 2103, e.g., for use in subsequent layers in a neural network.

An instruction fetch buffer 2109 connected to the controller 2104 for storing instructions used by the controller 2104;

the unified memory 2106, the input memory 2101, the weight memory 2102 and the instruction fetch memory 2109 are all On-Chip memories. The external memory is private to the NPU hardware architecture.

The operation of each layer in the recurrent neural network can be performed by the operation circuit 2103 or the vector calculation unit 2107.

Wherein any of the aforementioned processors may be a general purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits configured to control the execution of the programs of the method of the first aspect.

It should be noted that the above-described embodiments of the apparatus are merely schematic, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiments of the apparatus provided in the present application, the connection relationship between the modules indicates that there is a communication connection therebetween, and may be implemented as one or more communication buses or signal lines.

Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general hardware, and certainly can also be implemented by special hardware including application specific integrated circuits, special CLUs, special memories, special components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods described in the embodiments of the present application.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.

The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

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Text processing network, neural network training method and related equipment

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