@inproceedings{wang-etal-2020-chinese-grammatical,
title = "{C}hinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation",
author = "Wang, Yi and
Yuan, Ruibin and
Luo, Yan{`}gen and
Qin, Yufang and
Zhu, NianYong and
Cheng, Peng and
Wang, Lihuan",
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-1.10",
pages = "78--86",
abstract = "A better Chinese Grammatical Error Diagnosis (CGED) system for automatic Grammatical Error Correction (GEC) can benefit foreign Chinese learners and lower Chinese learning barriers. In this paper, we introduce our solution to the CGED2020 Shared Task Grammatical Error Correction in detail. The task aims to detect and correct grammatical errors that occur in essays written by foreign Chinese learners. Our solution combined data augmentation methods, spelling check methods, and generative grammatical correction methods, and achieved the best recall score in the Top 1 Correction track. Our final result ranked fourth among the participants.",
}
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%0 Conference Proceedings
%T Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation
%A Wang, Yi
%A Yuan, Ruibin
%A Luo, Yan‘gen
%A Qin, Yufang
%A Zhu, NianYong
%A Cheng, Peng
%A Wang, Lihuan
%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 wang-etal-2020-chinese-grammatical
%X A better Chinese Grammatical Error Diagnosis (CGED) system for automatic Grammatical Error Correction (GEC) can benefit foreign Chinese learners and lower Chinese learning barriers. In this paper, we introduce our solution to the CGED2020 Shared Task Grammatical Error Correction in detail. The task aims to detect and correct grammatical errors that occur in essays written by foreign Chinese learners. Our solution combined data augmentation methods, spelling check methods, and generative grammatical correction methods, and achieved the best recall score in the Top 1 Correction track. Our final result ranked fourth among the participants.
%U https://aclanthology.org/2020.nlptea-1.10
%P 78-86
Markdown (Informal)
[Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation](https://aclanthology.org/2020.nlptea-1.10) (Wang et al., NLP-TEA 2020)
ACL