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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model
%A Han, Yingjie
%A Yan, Yingjie
%A Han, Yangchao
%A Chao, Rui
%A Zan, Hongying
%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 han-etal-2020-chinese
%X 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.
%U https://aclanthology.org/2020.nlptea-1.13
%P 97-101

Markdown (Informal)

[Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model](https://aclanthology.org/2020.nlptea-1.13) (Han et al., NLP-TEA 2020)

• Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model (Han et al., NLP-TEA 2020)

ACL

• Yingjie Han, Yingjie Yan, Yangchao Han, Rui Chao, and Hongying Zan. 2020. Chinese Grammatical Error Diagnosis Based on RoBERTa-BiLSTM-CRF Model. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 97–101, Suzhou, China. Association for Computational Linguistics.