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2008

Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems

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Improving Alignments for Better Confusion Networks for Combining Machine Translation Systems
The state-of-the-art system combination method for machine translation (MT) is the word-based combination using confusion networks. One of the crucial steps in confusion network decoding is the alignment of different hypotheses to each other when building a network. In this paper, we present new methods to improve alignment of hypotheses using word synonyms and a two-pass alignment strategy. We demonstrate that combination with the new alignment technique yields up to 2.9 BLEU point improvement over the best input sys
Necip Fazil Ayan, Jing Zheng, Wen Wang
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Necip Fazil Ayan, Jing Zheng, Wen Wang
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