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        <title>Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis</title>
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            <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequence labeling problem, so as to detect Chinese grammatical errors, to identify the error types and to locate the error positions. In the corpora of this year’s shared task, there can be multiple errors in a single offset of a sentence, to address which, we simutaneously train three Bi-LSTM models sharing word embeddings which label Missing, Redundant and Selection errors respectively. We regard word ordering error as a special kind of word selection error which is longer during training phase, and then separate them by length during testing phase. In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our system achieved relatively high F1 for all the three levels in the traditional Chinese track and for the detection level in the Simpified Chinese track.</abstract>
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%0 Conference Proceedings
%T Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis
%A Huang, Shen
%A Wang, Houfeng
%Y Chen, Hsin-Hsi
%Y Tseng, Yuen-Hsien
%Y Ng, Vincent
%Y Lu, Xiaofei
%S Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F huang-wang-2016-bi
%X Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression. In this paper, we present a model based on Bidirectional Long Short-Term Memory(Bi-LSTM) neural networks, which treats the task as a sequence labeling problem, so as to detect Chinese grammatical errors, to identify the error types and to locate the error positions. In the corpora of this year’s shared task, there can be multiple errors in a single offset of a sentence, to address which, we simutaneously train three Bi-LSTM models sharing word embeddings which label Missing, Redundant and Selection errors respectively. We regard word ordering error as a special kind of word selection error which is longer during training phase, and then separate them by length during testing phase. In NLP-TEA 3 shared task for Chinese Grammatical Error Diagnosis(CGED), Our system achieved relatively high F1 for all the three levels in the traditional Chinese track and for the detection level in the Simpified Chinese track.
%U https://aclanthology.org/W16-4919
%P 148-154

Markdown (Informal)

[Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis](https://aclanthology.org/W16-4919) (Huang & Wang, NLP-TEA 2016)

• Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis (Huang & Wang, NLP-TEA 2016)

ACL

• Shen Huang and Houfeng Wang. 2016. Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis. In Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016), pages 148–154, Osaka, Japan. The COLING 2016 Organizing Committee.