C hinese Grammatical Error Detection Based on BERT Model (2020)

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.

## introduction with the development on economy and international influence of china,, more and more foreigners begin to learn chinese. however, chinese language is one of the most complex and difficult languages in the world, which is hard to master for foreigners. as a result, foreigners may produce a lot of grammatical errors in their written compositions which involve various error types such as word redundant error, word missing error, word selection error, and word disorder error @xcite . so it has become an important task and challenge for tcfl teachers to help foreign students to detect, understand and correct these grammatical errors. in recent years, natural language processing technology has been developing rapidly, and the latest deep learning technology has promoted the overall development of artificial intelligence greatly @xcite . in this background, computer-aided education has received more and more attention in nlp area, and one of the representative work is automatic chinese grammatical error diagnosis. in this task, computers are trained to detect and correct the grammatical errors in the compositions written by foreigners. and this work can provide a pretty assist on teaching second language writing. in 2020, the evaluation on chinese grammatical error diagnosis (cged) is held as a shared task in nlptea workshop. cged evaluation has been held for six times, which has greatly promoted the development of related technologies. we participated the cged 2020 as team ldu. we submitted three runs and achieved the second place in the fpr track. in this paper we will introduce our work in detail. ## related work early research on grammar error correction mainly focused on english. and the rule-based approach was popular in early research @xcite , for example, the grammar checker in mircrosoft word is a broad-coverage rule-based proofreading system. however, this system was designed for native speakers, and cannot detect errors in texts written by second language learners @xcite . with the development of machine learning technology, the data-driven based approach has become the gold standard method in the field of grammatical error correction @xcite @xcite . since 2011, four shared tasks are held to evaluate grammar error correction technology, that is the 2011 hoo shared task, the 2012 hoo shared task, the 2013 conll shared task, and the 2014 shared task (robert, 2011&2012; hwee, 2013&2014), these shared tasks has greatly promoted the ## conclusion this is a technical report of our ldu team participating in cged2020 shared task. we mainly participated in the task of grammar error detection. we regard the task as a binary classification task, and use different bert pretrained models and datasets in our experiments. the experimental results show that the bert model can greatly improve the accuracy of grammar error detection. and we conclude that the homogeneity and the smaples proportion distribution in training set and test set have a great impact on the final performance.