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Minimum Bayes Risk Decoding with Enlarged Hypothesis Space in System Combination

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Computational Linguistics and Intelligent Text Processing (CICLing 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7182))

Abstract

This paper describes a new system combination strategy in Statistical Machine Translation. Tromble et al. (2008) introduced the evidence space into Minimum Bayes Risk decoding in order to quantify the relative performance within lattice or n-best output with regard to the 1-best output. In contrast, our approach is to enlarge the hypothesis space in order to incorporate the combinatorial nature of MBR decoding. In this setting, we perform experiments on three language pairs ES-EN, FR-EN and JP-EN. For ES-EN JRC-Acquis our approach shows 0.50 BLEU points absolute and 1.9% relative improvement obver the standard confusion network-based system combination without hypothesis expansion, and 2.16 BLEU points absolute and 9.2% relative improvement compared to the single best system. For JP-EN NTCIR-8 the improvement is 0.94 points absolute and 3.4% relative, and for FR-EN WMT09 0.30 points absolute and 1.3% relative compared to the single best system, respectively.

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Authors and Affiliations

  1. School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

    Tsuyoshi Okita & Josef van Genabith

Authors
  1. Tsuyoshi Okita
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  2. Josef van Genabith
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Editors and Affiliations

  1. Center for Computing Research (CIC), National Polytechnic Institute (IPN), Mexico City, Mexico

    Alexander Gelbukh

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Okita, T., van Genabith, J. (2012). Minimum Bayes Risk Decoding with Enlarged Hypothesis Space in System Combination. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2012. Lecture Notes in Computer Science, vol 7182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28601-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-28601-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28600-1

  • Online ISBN: 978-3-642-28601-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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