Contextualized Character Representation for Chinese Grammatical Error Diagnosis

Jianbo Zhao, Si Li, Zhiqing Lin


Abstract
Nowadays, more and more people are learning Chinese as their second language. Establishing an automatic diagnosis system for Chinese grammatical error has become an important challenge. In this paper, we propose a Chinese grammatical error diagnosis (CGED) model with contextualized character representation. Compared to the traditional model using LSTM (Long-Short Term Memory), our model have better performance and there is no need to add too many artificial features.
Anthology ID:
W18-3725
Volume:
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
ACL | NLP-TEA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
172–179
Language:
URL:
https://www.aclweb.org/anthology/W18-3725
DOI:
10.18653/v1/W18-3725
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3725.pdf