In view of the problems such as exploding gradient or vanishing gradient or inefficiency caused by parallel problems when traditional neural networks deal with long text grammar error correction. In this paper, Chinese grammatical error detection is proposed as a sequence tagging problem for named entity recognition, and a Chinese grammatical error detection model based on BERT BILSTM CRF is designed and implemented by using Bert model to construct word vectors. Meanwhile, three groups of comparative tests were designed: CRF, BILSTM CRF and BERT CRF. In the end, BERT BILSTM CRF model achieves the error detection accuracy of 66.67% and 52.94% in M (missing error) and W (ordering error), respectively. BERT CRF and CRF models have also achieved good results in S (selection error) and R (redundant words). The research shows that it is feasible to solve grammatical error detection as a sequence labeling problem. On the other hand, a Chinese grammatical error detection model based on BERT BILSTM CRF is proposed for the first time.

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