? Exploiting Unlabeled Data for Neural Grammatical Error Detection
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (4) :758-767    DOI: 10.1007/s11390-017-1757-4
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Exploiting Unlabeled Data for Neural Grammatical Error Detection
Zhuo-Ran Liu1, Yang Liu2,3,4,5*, Member, CCF, ACM, IEEE
1 School of Software, Beihang University, Beijing 100191, China;
2 State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
3 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
4 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
5 Jiangsu Collaborative Innovation Center for Language Competence, Xuzhou 221009, China

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Abstract Identifying and correcting grammatical errors in the text written by non-native writers have received increasing attention in recent years. Although a number of annotated corpora have been established to facilitate data-driven grammatical error detection and correction approaches, they are still limited in terms of quantity and coverage because human annotation is labor-intensive, time-consuming, and expensive. In this work, we propose to utilize unlabeled data to train neural network based grammatical error detection models. The basic idea is to cast error detection as a binary classification problem and derive positive and negative training examples from unlabeled data. We introduce an attention-based neural network to capture long-distance dependencies that influence the word being detected. Experiments show that the proposed approach significantly outperforms SVM and convolutional networks with fixed-size context window.
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Keywordsunlabeled data   grammatical error detection   neural network     
Received 2016-12-20;
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This work is supported by the National Natural Science Foundation of China under Grant Nos. 61522204 and 61331013 and the National High Technology Research and Development 863 Program of China under Grant No. 2015AA015407. This research is also supported by the National Research Foundation of Singapore under its International Research Centre@Singapore Funding Initiative and administered by the IDM (Interactive Digital Media) Programme.

Corresponding Authors: Yang Liu     Email: liuyang2011@tsinghua.edu.cn
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Zhuo-Ran Liu, Yang Liu.Exploiting Unlabeled Data for Neural Grammatical Error Detection[J]  Journal of Computer Science and Technology, 2017,V32(4): 758-767
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http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-017-1757-4
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