Model training method, text error correction method and related products

By introducing multiple error types of model training data and using masking and disrupting processing techniques to train text error correction models, the problem of low accuracy of text error correction in the prior art is solved, and efficient identification and correction of multiple text errors is achieved.

CN119647520APending Publication Date: 2025-03-18AGRICULTURAL BANK OF CHINA
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Patent Information

Application Number
CN202411715667.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-27
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

Existing text error correction methods are mainly used to identify spelling errors, and cannot fully cover all possible text error types, resulting in low error correction accuracy.

Method used

By obtaining model training data containing multiple error types such as spelling errors, grammar errors, logical errors and format errors, the first sub-model of the machine learning model is used to mask the word segmentation results of text data, and the second sub-model is used to mess the word segmentation results, and the text error correction model is trained based on the labeled data, the results after masking and the results after messing.

Benefits of technology

This method can fully cover and identify various errors that may exist in the text to be corrected, enhances the machine learning model's ability to recognize error patterns and learn the correct text structure, and improves the accuracy of the text error correction model.

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Abstract

The embodiment of the invention provides a model training method, a text error correction method and a related product, and relates to the technical field of machine learning. According to the model training method provided by the invention, by introducing the model training data including various error types such as spelling errors, grammar errors, logic errors and format errors, various errors possibly existing in the to-be-corrected text can be comprehensively covered and recognized. Moreover, the word segmentation result of the text data is subjected to masking processing through a first sub-model of the machine learning model, the word segmentation result of the text data is subjected to disordering processing through a second sub-model, and the machine learning model is trained according to the annotation data, the result after masking processing and the result after disordering processing; the recognition capability of the machine learning model for the error mode is enhanced, the learning capability of the machine learning model for the correct text structure is also promoted, and the text error correction model shows higher accuracy during text error correction.
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