Improved statistical machine translation model with topic-based paraphrase

被引：0

作者：

Su, Jin-Song ^{[1
]}

Dong, Huai-Lin ^{[1
]}

Chen, Yi-Dong ^{[2
]}

Shi, Xiao-Dong ^{[2
]}

Wu, Qing-Qiang ^{[1
]}

机构：

[1] School of Software, Xiamen University, Xiamen

[2] Department of Cognitive Science, Xiamen University, Xiamen

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2014年 / 48卷 / 10期

关键词：

Paraphrase; Statistical machine translation; Topic model;

D O I：

10.3785/j.issn.1008-973X.2014.10.019

中图分类号：

学科分类号：

摘要：

To deal with the defect of the conventional parallel corpus based paraphrase extraction method which neglects document-level context, the paraphrase extraction and its application in statistical machine translation were improved by introducing the context based on topic model. The problem that how to better learn two kinds of paraphrase probabilities: topic-insensitive and topic-sensitive ones, was mainly analyzed. Both of the two probabilities can be incorporated into the modeling of statistical machine translation by using different methods. The experimental results on various test sets demonstrated the effectiveness of the approach. ©, 2014, Zhejiang University. All right reserved.

引用

页码：1843 / 1849

页数：6

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