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Factored Statistical Machine Translation for Grammatical Error Correction
Wang, Yiming; Longyue Wang; Derek F. Wong; Lidia S. Chao; Xiaodong Zeng; Yi Lu
2014
Conference Namethe Eighteenth Conference on Computational Natural Language Learning: Shared Task
Source PublicationProceedings of, pages, ,
Pages83–90
Conference Date26-27 July 2014
Conference PlaceBaltimore, Maryland
Abstract

This paper describes our ongoing work on grammatical error correction (GEC). Focusing on all possible error types in a real-life environment, we propose a factored statistical machine translation (SMT) model for this task. We consider error correction as a series of language translation problems guided by various linguistic information, as factors that influence translation results. Factors included in our study are morphological information, i.e. word stem, prefix, suffix, and Part-of-Speech (PoS) information. In addition, we also experimented with different combinations of translation models (TM), phrase-based and factor-based, trained on various datasets to boost the overall performance. Empirical results show that the proposed model yields an improvement of 32.54% over a baseline phrase-based SMT model. The system participated in the CoNLL 2014 shared task and achieved the 7 th and 5 th F0.5 scores1 on the official test set among the thirteen participating teams.

Language英语
Fulltext Access
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationNatural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information Science, University of Macau, Macau S.A.R., China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang, Yiming,Longyue Wang,Derek F. Wong,et al. Factored Statistical Machine Translation for Grammatical Error Correction[C],2014:83–90.
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