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Effective Hypotheses Re-ranking Model in Statistical Machine Translation

  • Yiming Wang
  • Longyue Wang
  • Derek F. Wong
  • Lidia S. Chao
Part of the Communications in Computer and Information Science book series (CCIS, volume 493)

Abstract

In statistical machine translation, an effective way to improve the translation quality is to regularize the posterior probabilities of translation hypotheses according to the information of N-best list. In this paper, we present a novel approach to improve the final translation result by dynamically augmenting the translation scores of hypotheses that derived from the N-best translation candidates. The proposed model was trained on a general domain UM-Corpus and evaluated on IWSLT Chinese-English TED Talk data under the configurations of document level translation and sentence level translation respectively. Empirical results real that sentence level translation model outperforms the document level and the baseline system.

Keywords

Phrase-Based Machine Translation N-best List Hypotheses Re-Ranking Hypotheses Re-decoding 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yiming Wang
    • 1
  • Longyue Wang
    • 1
  • Derek F. Wong
    • 1
  • Lidia S. Chao
    • 1
  1. 1.Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory, Department of Computer and Information ScienceUniversity of MacauMacauChina

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