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EMNLP
2007

A Systematic Comparison of Training Criteria for Statistical Machine Translation

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A Systematic Comparison of Training Criteria for Statistical Machine Translation
We address the problem of training the free parameters of a statistical machine translation system. We show significant improvements over a state-of-the-art minimum error rate training baseline on a large ChineseEnglish translation task. We present novel training criteria based on maximum likelihood estimation and expected loss computation. Additionally, we compare the maximum a-posteriori decision rule and the minimum Bayes risk decision rule. We show that, not only from a theoretical point of view but also in terms of translation quality, the minimum Bayes risk decision rule is preferable.
Richard Zens, Sasa Hasan, Hermann Ney
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where EMNLP
Authors Richard Zens, Sasa Hasan, Hermann Ney
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