# A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging
## 2 Related Work  
Before the rise of deep learning, almost all Chinese GEC researchers used methods based on rules and statistics [8; 9]. Most of these methods extracted features manually, making it easy to produce deviation. The effect of these methods was not ideal, due to the complexity of Chinese grammar. As deep learning began to dominate in various fields, text error correction gradually began to be combined with deep learning. In 2014, [PERSON] et al. [10] organized a shared task for Chinese grammatical error diagnosis (CGED), which greatly assisted the research on Chinese GEC. There are already many ways to deal with Chinese GEC issues. At present, the most typical methods for dealing with Chinese GEC are sequence generation methods and sequence tagging methods. The sequence generation approach directly generates the correct sentence from the original sentence, similarly to a machine translation task. [PERSON] et al. [11] used a Seq2 Seq model for Chinese GEC and pre-trained the model using pseudo data. Since there may be multiple errors in a sentence which cannot be corrected by one round of editing operations, [PERSON] et al. [12] proposed an iterative error correction strategy. This method takes the results with the highest confidence generated in the first round as the input of the second round, and repeats the process until there is no need for further correction or until the maximum number of cycles is reached. Since there is a large intersection between the original text and the target text of the Seq2 Seq model, generating a large number of correct words will waste resource. [PERSON] et al. [13] introduced the ConvSeq2 Seq [14] model for the Chinese GEC task, which enabled the parallel execution of the decoding process and improved the decoding speed. Their single model achieved a 29.0% F0.5 measure on the NLPCC 2018 test set. [PERSON] et al. [15] divided the GEC task into two subtasks: error span detection (ESD) and error span correction (ESC). ESD uses sequence tagging binary classification to identify the location of the incorrect character, and then ESC uses the Seq2 Seq model to correct errors only for the wrong locations. The inference time for this method is only half of that for Seq2 Seq used directly, and the F0.5 measure is 28.4% on the NLPCC 2018 test set. With the rise of pre-trained language models, the pre-trained model BERT [16] achieved better results in multiple NLP tasks. [PERSON] et al. [17] proposed a BERT-fused method that uses BERT to extract the features of input sequences, and then fuses the representation with each encoder and decoder layer of the NMT model through an attention mechanism. [PERSON] et al. [18] experimented on the Chinese GEC task using BERT-fused and used BERT to initialize the Transformer [19] encoder and randomly initialize the decoder, achieving better results than BERT-fused on the NLPCC 2018 dataset. [PERSON] et al. [20] fine-tuned BERT using the GEC corpus and then trained the BERT-based Seq2 Seq model with the original input and the BERT output. [PERSON] and [PERSON] [21] proposed an end-to-end non-autoregressive sequence prediction model for Chinese GEC, to solve the problem that the label space of the sequence labeling method is large and requires multiple rounds of decoding. They achieved good results on a fixed-length dataset. GEC methods based on sequence tagging are intended to solve the problems of slow inference speed and poor controllability of generation. In GEC, the intersection of the error text and the correct text is particularly large, so it can be related to the way that the string edit distance is related to how the incorrect text can be transformed into the correct text with fewer token edit operations. [PERSON] et al. [2] proposed a parallel iterative editing (PIE) model which defines editing operations such as copy, append, delete, replace and case-change. By comparing the error sentence with the correct sentence, the editing method is obtained, and then each word is marked, iteratively refining the prediction to capture dependencies.