A discriminative global training algorithm for statistical MT

Christoph Tillmann, Tong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel training algorithm for a linearly-scored block sequence translation model. The key component is a new procedure to directly optimize the global scoring function used by a SMT decoder. No translation, language, or distortion model probabilities are used as in earlier work on SMT. Therefore our method, which employs less domain specific knowledge, is both simpler and more extensible than previous approaches. Moreover, the training procedure treats the decoder as a black-box, and thus can be used to optimize any decoding scheme. The training algorithm is evaluated on a standard Arabic-English translation task.

Original languageEnglish (US)
Title of host publicationCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages721-728
Number of pages8
ISBN (Print)1932432655, 9781932432657
DOIs
StatePublished - 2006
Externally publishedYes
Event21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW, Australia
Duration: Jul 17 2006Jul 21 2006

Publication series

NameCOLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume1

Other

Other21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
Country/TerritoryAustralia
CitySydney, NSW
Period7/17/067/21/06

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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