Text error correction model training method and device, text error correction model recognition method and device, equipment and storage medium

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a soft mask-based text error correction model training method and device, a soft mask-based text error correctionmodel recognition method and device, computer equipment and a computer readable storage medium. The method comprises the steps of obtaining a to-be-modified text, and converting the to-be-modified text into word vector information of each word; training a preset soft mask language model according to the word vector information of each word to obtain a corresponding loss function; updating model parameters of the preset soft mask language model based on a loss function, and determining whether the preset soft mask language model is in a convergence state or not; if it is determined that the preset soft mask language model is in the convergence state, generating a corresponding text error correction model, and processing words are through soft masks, so that under the condition that a largenumber of training expectations are not needed, the training duration of the model is shortened, data is fitted, and the accuracy of the model is improved.

Description

technical field [0001] The present application relates to the field of artificial intelligence technology, and in particular to a soft mask-based text error correction model training method, recognition method, device, computer equipment, and computer-readable storage medium. Background technique [0002] Text error correction has always been a scene that is more concerned about the current natural language, for example, to correct the minutes of the meeting or the official documents of the government. The text error correction grammar models currently used in the market are divided into two categories: machine learning models, which generate candidates and select the best candidates for replacement through error recognition; the other is deep learning models, which use sequence-to-sequence grammar correction wrong way. However, the machine learning model cannot fit the data, resulting in low accuracy; while the deep learning model requires a large amount of corpus, which r...

AI Technical Summary

Problems solved by technology

[0003] The main purpose of this application is to provide a soft mask-based text error correction model training method, recognition method, device, computer equipment, and computer-readable storage medium, aiming to solve t

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