NDLI: A Topic-Triggered Translation Model for Statistical Machine Translation

Content Provider	IET Digital Library
Author	Su, Jinsong Wang, Zhihao Wu, Qingqiang Yao, Junfeng Long, Fei Zhang, Haiying
Abstract	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.
Starting Page	65
Ending Page	72
Page Count	8
ISSN	10224653
Volume Number	26
e-ISSN	20755597
Issue Number	Issue 1, Jan (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/26/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2016.10.007
Journal	Chinese Journal of Electronics
Publisher Date	2017-01-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Bilingual Translation Units Language Translation Machine Translation Online Score Selected Translation Rules Probability Statistical Distribution Statistical Machine Translation Statistics Topic Posterior Distribution Topic-specific Translation Probability Estimation Topic-triggered Translation Model Topical Context Information
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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