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.