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Statistical Machine Translation by Generalized Parsing

I. D. Melamed,Wei Wang,L. Weaver

Published 2004 in arXiv: Computation and Language

ABSTRACT

Designers of statistical machine translation (SMT) systems have begun to employ tree-structured translation models. Systems involving tree-structured translation models tend to be complex. This article aims to reduce the conceptual complexity of such systems, in order to make them easier to design, implement, debug, use, study, understand, explain, modify, and improve. In service of this goal, the article extends the theory of semiring parsing to arrive at a novel abstract parsing algorithm with five functional parameters: a logic, a grammar, a semiring, a search strategy, and a termination condition. The article then shows that all the common algorithms that revolve around tree-structured translation models, including hierarchical alignment, inference for parameter estimation, translation, and structured evaluation, can be derived by generalizing two of these parameters -- the grammar and the logic. The article culminates with a recipe for using such generalized parsers to train, apply, and evaluate an SMT system that is driven by tree-structured translation models.

PUBLICATION RECORD

  • Publication year

    2004

  • Venue

    arXiv: Computation and Language

  • Publication date

    2004-07-01

  • Fields of study

    Linguistics, Computer Science

  • Identifiers
    arXiv cs/0407005
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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