Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task
Hongying Zan | Yangchao Han | Haotian Huang | Yingjie Yan | Yuke Wang | Yingjie Han
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.