Rui Chao


融入篇章信息的文学作品命名实体识别(Document-level Literary Named Entity Recognition)
Yuxiang Jia (贾玉祥) | Rui Chao (晁睿) | Hongying Zan (昝红英) | Huayi Dou (窦华溢) | Shuai Cao (曹帅) | Shuo Xu (徐硕)
Proceedings of the 20th Chinese National Conference on Computational Linguistics



Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model
Yingjie Han | Yingjie Yan | Yangchao Han | Rui Chao | Hongying Zan
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications

Chinese Grammatical Error Diagnosis (CGED) is a natural language processing task for the NLPTEA6 workshop. The goal of this task is to automatically diagnose grammatical errors in Chinese sentences written by L2 learners. This paper proposes a RoBERTa-BiLSTM-CRF model to detect grammatical errors in sentences. Firstly, RoBERTa model is used to obtain word vectors. Secondly, word vectors are input into BiLSTM layer to learn context features. Last, CRF layer without hand-craft features work for processing the output by BiLSTM. The optimal global sequences are obtained according to state transition matrix of CRF and adjacent labels of training data. In experiments, the result of RoBERTa-CRF model and ERNIE-BiLSTM-CRF model are compared, and the impacts of parameters of the models and the testing datasets are analyzed. In terms of evaluation results, our recall score of RoBERTa-BiLSTM-CRF ranks fourth at the detection level.