NDLI: Improved Graph-Based Bilingual Corpus Selection with Sentence Pair Ranking for Statistical Machine Translation

Content Provider	IEEE Xplore Digital Library
Author	WenHan Chao ZhouJun Li
Copyright Year	2011
Abstract	In statistical machine translation, the number of sentence pairs in the bilingual corpus is very important to the quality of translation. However, when the quantity reaches some extent, enlarging corpus has less effect on the translation, whereas increasing greatly the time and space complexity to building translation systems, which hinders the development of statistical machine translation. In this paper, we propose several ranking approaches to measure the quantity of information of each sentence pair, and apply them into a graph-based bilingual corpus selection framework to form an improved corpus selection approach, which now considers the difference of the initial quantities of information between the sentence pairs. Our experiments in a Chinese-English translation task show that, selecting only 50% of the whole corpus via the graph-based selection approach as training set, we can obtain the near translation result with the one using the whole corpus, and we obtain better results than the baselines after using the IDF-related ranking approach.
Starting Page	446
Ending Page	451
File Size	197883
Page Count	6
File Format	PDF
ISBN	9781457720680
ISSN	10823409
e-ISBN	9780769545967
DOI	10.1109/ICTAI.2011.73
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Measurement Redundancy Mathematical model Equations Educational institutions Vocabulary Graph SMT Ranking Corpus Selection
Content Type	Text
Resource Type	Article

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