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A Topic-Triggered Translation Model for Statistical Machine Translation

Chinese Journal of Electronics 2017年第1期26卷 65-72页

作者： SU Jinsong WANG Zhihao WU Qingqiang YAO Junfeng LONG Fei ZHANG Haiying Software School Xiamen University Provincial Key Laboratory for Computer information Processing Technology Soochow University Automation Department Xiamen University

Translation model containing translation rules with probabilities plays a crucial role in statistical machine translation. Conventional method estimates translation probabilities with only the consideration of cooccurrence frequencies of bilingual translation units, while ignoring document-level context information. In this paper, we extend the conventional translation model to a topic-triggered one. Specifically, we estimate topic-specific translation probabilities of translation rules by leveraging topical context information, and online score selected translation rules according to topic posterior distributions of translated sentences. As compared with the conventional model, our model allows for more fine-grained distinction among different translations. Experiment results on large data set demonstrate the effectiveness of our model.

关键词： Statistical machine translation Topictriggered translation model topical context information

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Topic-aware pivot language approach for statistical machine translation

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第4期15卷 241-253页

作者： Jin-song SU Xiao-dong SHI Yan-zhou HUANG Yang LIU Qing-qiang WU Yi-dong CHEN Huai-lin DONG Software School Xiamen University Center for Digital Media Computing Xiamen University Cognitive Science Department Xiamen University Department of Computer Science and Technology Tsinghua University

The pivot language approach for statistical machine translation(SMT) is a good method to break the resource bottleneck for certain language pairs. However, in the implementation of conventional approaches, pivotside context information is far from fully utilized, resulting in erroneous estimations of translation probabilities. In this study, we propose two topic-aware pivot language approaches to use different levels of pivot-side context. The first method takes advantage of document-level context by assuming that the bridged phrase pairs should be similar in the document-level topic distributions. The second method focuses on the effect of local context. Central to this approach are that the phrase sense can be reflected by local context in the form of probabilistic topics, and that bridged phrase pairs should be compatible in the latent sense distributions. Then, we build an interpolated model bringing the above methods together to further enhance the system performance. Experimental results on French-Spanish and French-German translations using English as the pivot language demonstrate the effectiveness of topic-based context in pivot-based SMT.

关键词： Natural language processing Pivot-based statistical machine translation topical context information

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