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Phrase-Based Statistical Machine Translation

  • Richard Zens
  • Franz Josef Och
  • Hermann Ney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2479)

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.

Keywords

Target Sentence Translation Model Word Error Rate Sentence Pair Source Sentence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Richard Zens
    • 1
  • Franz Josef Och
    • 1
  • Hermann Ney
    • 1
  1. 1.Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science DepartmentRWTH Aachen — University of TechnologyGermany

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