@inproceedings{cheng-duan-2020-chinese,
    title = "{C}hinese Grammatical Error Detection Based on {BERT} Model",
    author = "Cheng, Yong  and
      Duan, Mofan",
    editor = "YANG, Erhong  and
      XUN, Endong  and
      ZHANG, Baolin  and
      RAO, Gaoqi",
    booktitle = "Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications",
    month = dec,
    year = "2020",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.nlptea-1.15/",
    doi = "10.18653/v1/2020.nlptea-1.15",
    pages = "108--113",
    abstract = "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."
}