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    <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.</abstract>
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%0 Conference Proceedings
%T Contextualized Character Representation for Chinese Grammatical Error Diagnosis
%A Zhao, Jianbo
%A Li, Si
%A Lin, Zhiqing
%Y Tseng, Yuen-Hsien
%Y Chen, Hsin-Hsi
%Y Ng, Vincent
%Y Komachi, Mamoru
%S Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhao-etal-2018-contextualized
%X 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.
%R 10.18653/v1/W18-3725
%U https://aclanthology.org/W18-3725
%U https://doi.org/10.18653/v1/W18-3725
%P 172-179

Markdown (Informal)

[Contextualized Character Representation for Chinese Grammatical Error Diagnosis](https://aclanthology.org/W18-3725) (Zhao et al., NLP-TEA 2018)

• Contextualized Character Representation for Chinese Grammatical Error Diagnosis (Zhao et al., NLP-TEA 2018)

ACL

• Jianbo Zhao, Si Li, and Zhiqing Lin. 2018. Contextualized Character Representation for Chinese Grammatical Error Diagnosis. In Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pages 172–179, Melbourne, Australia. Association for Computational Linguistics.