Phrase-Based Statistical Machine Translation

  • Authors:
  • Richard Zens;Franz Josef Och;Hermann Ney

  • Affiliations:
  • -;-;-

  • Venue:
  • KI '02 Proceedings of the 25th Annual German Conference on AI: Advances in Artificial Intelligence
  • Year:
  • 2002

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Abstract

This paper is based on the work carried out in the framework of the VERBMOBIL project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches.In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is that it does not take contextual information into account for the translation decisions. We will present a translation model that is based on bilingual phrases to explicitly model the local context. We will show that this model performs better than the single-word based model. We will compare monotone and non-monotone search for this model and we will investigate the benefit of using the sum criterion instead of the maximum approximation.