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A phrase-based, joint probability model for statistical machine translation

Published:06 July 2002Publication History

ABSTRACT

We present a joint probability model for statistical machine translation, which automatically learns word and phrase equivalents from bilingual corpora. Translations produced with parameters estimated using the joint model are more accurate than translations produced using IBM Model 4.

References

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  1. A phrase-based, joint probability model for statistical machine translation

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        • Published in

          cover image DL Hosted proceedings
          EMNLP '02: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
          July 2002
          328 pages

          Publisher

          Association for Computational Linguistics

          United States

          Publication History

          • Published: 6 July 2002

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          • Article

          Acceptance Rates

          Overall Acceptance Rate73of234submissions,31%

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