Text classification model training method, device and equipment and storage medium

Abstract

The invention discloses a text classification model training method, device and equipment and a storage medium and belongs to the field of artificial intelligence. According to the method, on one hand, the adversarial sample is introduced, and the text classification model is trained by using the text sample and the adversarial sample, so the text classification model learns the classification method for the text added with disturbance, and thereby the robustness of the text classification model is improved, and accuracy of text classification is improved; on the other hand, the text classification model can reconstruct the text features of the adversarial samples extracted during classification and restore the text features into text content, so interpretability of the adversarial training method is improved. According to the method, the model parameters are trained by combining the errors between the reconstructed text content and the text content of the text sample, so the text classification model can extract more accurate text features, namely, more accurate feature expression of the text content is obtained, and robustness and accuracy of feature extraction of the text classification model are improved.

Description

technical field [0001] The present application relates to the field of artificial intelligence, and in particular, to a text classification model training method, apparatus, device and storage medium. Background technique [0002] Artificial intelligence is applied in various fields, and replacing human work based on artificial intelligence can greatly improve the efficiency of business processing. In terms of text classification, the text classification model can be trained to obtain a trained text classification model, and the text to be classified can be input into the trained text classification model to predict the type of the text. [0003] At present, the text classification model training method usually obtains text samples, classifies the text samples based on the text classification model, obtains the predicted classification results, and updates the model parameters according to the predicted classification results and the target classification results carried by ...

AI Technical Summary

Problems solved by technology

[0004] The robustness of the text classification model trained by the above method is very poor. If some small perturbations are added to the input text, the text classification model will be misclassified.

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