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COLING
2008

Regenerating Hypotheses for Statistical Machine Translation

9 years 4 months ago
Regenerating Hypotheses for Statistical Machine Translation
This paper studies three techniques that improve the quality of N-best hypotheses through additional regeneration process. Unlike the multi-system consensus approach where multiple translation systems are used, our improvement is achieved through the expansion of the Nbest hypotheses from a single system. We explore three different methods to implement the regeneration process: redecoding, n-gram expansion, and confusion network-based regeneration. Experiments on Chinese-to-English NIST and IWSLT tasks show that all three methods obtain consistent improvements. Moreover, the combination of the three strategies achieves further improvements and outperforms the baseline by 0.81 BLEU-score on IWSLT'06, 0.57 on NIST'03, 0.61 on NIST'05 test set respectively.
Boxing Chen, Min Zhang, AiTi Aw, Haizhou Li
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Boxing Chen, Min Zhang, AiTi Aw, Haizhou Li
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