Extending statistical machine translation with discriminative and trigger-based lexicon models

  • Authors:
  • Arne Mauser;Saša Hasan;Hermann Ney

  • Affiliations:
  • RWTH Aachen University, Germany;RWTH Aachen University, Germany;RWTH Aachen University, Germany

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
  • Year:
  • 2009

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Abstract

In this work, we propose two extensions of standard word lexicons in statistical machine translation: A discriminative word lexicon that uses sentence-level source information to predict the target words and a trigger-based lexicon model that extends IBM model 1 with a second trigger, allowing for a more fine-grained lexical choice of target words. The models capture dependencies that go beyond the scope of conventional SMT models such as phrase-and language models. We show that the models improve translation quality by 1% in BLEU over a competitive baseline on a large-scale task.