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Using N-gram based Features for Machine Translation

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by Yong Zhao, et al.
Citations:7 - 1 self
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BibTeX

@MISC{Zhao_usingn-gram,
    author = {Yong Zhao and et al.},
    title = {Using N-gram based Features for Machine Translation},
    year = {}
}

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Abstract

Conventional confusion network based system combination for machine translation (MT) heavily relies on features that are based on the measure of agreement of words in different translation hypotheses. This paper presents two new features that consider agreement of n-grams in different hypotheses to improve the performance of system combination. The first one is based on a sentence specific online n-gram language model, and the second one is based on n-gram voting. Experiments on a large scale Chinese-to-English MT task show that both features yield significant improvements on the translation performance, and a combination of them produces even better translation results.

Keyphrases

machine translation    system combination    significant improvement    conventional confusion network    n-gram voting    first one    second one    different translation hypothesis    translation result    different hypothesis    translation performance    new feature   

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