关键词: 文本纠错, 预训练模型, 层次化修正框架, 深度学习

Abstract: Redundant expressions, misuse of words, and missing content and other text errors can seriously affect the interpretation of text semantics. There exist two major problems with current text error correction models: The Encoder-Decoder based text error correction models have slow decoding speed; text error detection task and text correction task are handled as two separate tasks. Hence, a text error correction model based on a hierarchical editing framework is proposed in this paper. Firstly, a variety of text semantic representations are obtained through modelling pre-trained model. Secondly, text errors are located by using these text semantic representations. Finally, on the basis of hierarchical editing framework, precise editing operations are worked out to edit the errors. Experiments on the published text error correction dataset show that the proposed model has faster decoding speed and higher recall rate than comparison models.

Key words: text error correction, pre-trained model, hierarchical editing framework, deep learning