Discriminative Training and Maximum Entropy Models for Statistical Machine Translation

F. Och,H. Ney

Published 2002 in Annual Meeting of the Association for Computational Linguistics

ABSTRACT

We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source-channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible hidden variables. This approach allows a baseline machine translation system to be extended easily by adding new feature functions. We show that a baseline statistical machine translation system is significantly improved using this approach.

PUBLICATION RECORD

  • Publication year

    2002

  • Venue

    Annual Meeting of the Association for Computational Linguistics

  • Publication date

    2002-07-06

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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