@inproceedings{zan-etal-2020-chinese,
title = "{C}hinese Grammatical Errors Diagnosis System Based on {BERT} at {NLPTEA}-2020 {CGED} Shared Task",
author = "Zan, Hongying and
Han, Yangchao and
Huang, Haotian and
Yan, Yingjie and
Wang, Yuke and
Han, Yingjie",
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://aclanthology.org/2020.nlptea-1.14",
pages = "102--107",
abstract = "In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.",
}
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<abstract>In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.</abstract>
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%0 Conference Proceedings
%T Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task
%A Zan, Hongying
%A Han, Yangchao
%A Huang, Haotian
%A Yan, Yingjie
%A Wang, Yuke
%A Han, Yingjie
%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 zan-etal-2020-chinese
%X In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.
%U https://aclanthology.org/2020.nlptea-1.14
%P 102-107
Markdown (Informal)
[Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task](https://aclanthology.org/2020.nlptea-1.14) (Zan et al., NLP-TEA 2020)
ACL