Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation

Yi Wang, Ruibin Yuan, Yan‘gen Luo, Yufang Qin, NianYong Zhu, Peng Cheng, Lihuan Wang


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.
Anthology ID:
2020.nlptea-1.10
Volume:
Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
NLP-TEA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
78–86
Language:
URL:
https://aclanthology.org/2020.nlptea-1.10
DOI:
Bibkey:
Cite (ACL):
Yi Wang, Ruibin Yuan, Yan‘gen Luo, Yufang Qin, NianYong Zhu, Peng Cheng, and Lihuan Wang. 2020. Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation. In Proceedings of the 6th Workshop on Natural Language Processing Techniques for Educational Applications, pages 78–86, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Chinese Grammatical Error Correction Based on Hybrid Models with Data Augmentation (Wang et al., NLP-TEA 2020)
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PDF:
https://aclanthology.org/2020.nlptea-1.10.pdf