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A Weighted Finite State Transducer Implementation of the Alignment Template Model for Statistical Machine Translation

Shankar Kumar,W. Byrne

Published 2003 in North American Chapter of the Association for Computational Linguistics

ABSTRACT

We present a derivation of the alignment template model for statistical machine translation and an implementation of the model using weighted finite state transducers. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard FSM operations involving these transducers. One of the benefits of using this framework is that it obviates the need to develop specialized search procedures, even for the generation of lattices or N-Best lists of bitext word alignments and translation hypotheses. We evaluate the implementation of the model on the French-to-English Hansards task and report alignment and translation performance.

PUBLICATION RECORD

  • Publication year

    2003

  • Venue

    North American Chapter of the Association for Computational Linguistics

  • Publication date

    2003-05-27

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.3115/1073445.1073464
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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