# A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging
## 2 Related Work
This method greatly improves the error correction speed, but it has not been applied to Chinese to date. [PERSON] et al. [22] proposed a sequence tagging error correction method, LaserTagger, which set up two labels for retain and delete, and added phrase or word labels based on these two labels. The entire prediction space was twice the size of the set thesaurus. The model uses BERT as the encoder and Transformer as the decoder for sequence tagging. However, LaserTagger does not seem to work well in Chinese; it only achieved a 19.9% F0.5 measure on the NLPCC 2018 test set. [PERSON] et al. [3] proposed the GECToR model and defined more than 5000 tags for grammatical operations, including basic tags such as KEEP, DELETE, 1167 APPENDIX_X and 3802 REPLACE_X, as well as 29 new tags for grammatical changes in English, such as singular and plural conversion, tense conversion and so on. Firstly, each token of the wrong sentence is labeled with a defined label and then trained by using the sequence tagging model based on BERT, and the iterative inference is used for multiple rounds of error correction. This has achieved the best effect in English GEC. In the NLPTEA 2020 [23] competition, [PERSON] et al. [24] applied the GECToR model to Chinese GEC for the first time. They removed the 29 grammar change tags for English and constructed 8772 basic tags for common Chinese characters. Their joint model achieved the highest F1 value on the NLPTEA 2020 error correction top1 subtask. We experimented with the GECToR model on the NLPTC 2018 test set and achieved a 29.8% F0.5 measure. Due to the high cost of GEC data annotation, it is difficult to obtain large-scale training data. Many studies related to grammar error correction have focused on how to automatically construct large-scale training data. It has been shown that the use of data augmentation methods can effectively improve the performance of grammar error correction models. [PERSON] and [PERSON] [25] proposed a Chinese error correction method called MaskGEC, which uses a dynamic random mask to mask and replace some words, to expand the dataset. Surprisingly, the MaskGEC achieved a good score of 36.9% on the F0.5 measure on the NLPCC 2018 test dataset. [PERSON] et al. [26] proposed a data augmentation method that fuses word and character levels. They used the expanded six million sentence pairs to train the model and achieved a 39.1% F0.5 measure on the NLPCC 2018 test set. Because our focus was on the training strategy and model improvement, we expected the model to achieve better results on the same sized dataset. Therefore, no comparisons are made with methods that use data augmentation. Among the above methods, the sequence generation methods such as Seq2 Seq, ConvSeq2 Seq, ESD and BERT-fused can be applied without modification to other languages. Only the training data of the corresponding language needs to be used for training. In contrast, sequence tagging methods such as PIE, LaserTagger, GECToR, etc. must set specific edit tags for other languages. Good or bad edit tag setting has a large impact on the performance of the model, so the settings cannot be directly applied to other languages. For the NLPCC 2018 dataset, the best-performing sequence generation method is BERT-fused, with a 29.9% F0.5 value. GECToR is the best-performing sequence labeling method, with a 29.8% F0.5 value, and the inference speed of GECToR is much faster than that of BERT-fused.