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EMNLP
2009

Effective Use of Linguistic and Contextual Information for Statistical Machine Translation

8 years 9 months ago
Effective Use of Linguistic and Contextual Information for Statistical Machine Translation
Current methods of using lexical features in machine translation have difficulty in scaling up to realistic MT tasks due to a prohibitively large number of parameters involved. In this paper, we propose methods of using new linguistic and contextual features that do not suffer from this problem and apply them in a state-ofthe-art hierarchical MT system. The features used in this work are non-terminal labels, non-terminal length distribution, source string context and source dependency LM scores. The effectiveness of our techniques is demonstrated by significant improvements over a strong baseline. On Arabic-to-English translation, improvements in lower-cased BLEU are
Libin Shen, Jinxi Xu, Bing Zhang, Spyros Matsoukas
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EMNLP
Authors Libin Shen, Jinxi Xu, Bing Zhang, Spyros Matsoukas, Ralph M. Weischedel
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