%0 Conference Proceedings %T Chinese Grammatical Error Detection Based on BERT Model %A Cheng, Yong %A Duan, Mofan %Y YANG, Erhong %Y XUN, Endong %Y ZHANG, Baolin %Y RAO, Gaoqi %S Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications %D 2020 %8 December %I Association for Computational Linguistics %C Suzhou, China %F cheng-duan-2020-chinese %X Automatic grammatical error correction is of great value in assisting second language writing. In 2020, the shared task for Chinese grammatical error diagnosis(CGED) was held in NLP-TEA. As the LDU team, we participated the competition and submitted the final results. Our work mainly focused on grammatical error detection, that is, to judge whether a sentence contains grammatical errors. We used the BERT pre-trained model for binary classification, and we achieve 0.0391 in FPR track, ranking the second in all teams. In error detection track, the accuracy, recall and F-1 of our submitted result are 0.9851, 0.7496 and 0.8514 respectively. %U https://aclanthology.org/2020.nlptea-1.15 %P 108-113