Enhancing language models in statistical machine translation with backward n-grams and mutual information triggers

  • Authors:
  • Deyi Xiong;Min Zhang;Haizhou Li

  • Affiliations:
  • Human Language Technology, Institute for Infocomm Research, Singapore;Human Language Technology, Institute for Infocomm Research, Singapore;Human Language Technology, Institute for Infocomm Research, Singapore

  • Venue:
  • HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
  • Year:
  • 2011

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Abstract

In this paper, with a belief that a language model that embraces a larger context provides better prediction ability, we present two extensions to standard n-gram language models in statistical machine translation: a backward language model that augments the conventional forward language model, and a mutual information trigger model which captures long-distance dependencies that go beyond the scope of standard n-gram language models. We integrate the two proposed models into phrase-based statistical machine translation and conduct experiments on large-scale training data to investigate their effectiveness. Our experimental results show that both models are able to significantly improve translation quality and collectively achieve up to 1 BLEU point over a competitive baseline.