Semantic Roles for SMT: A Hybrid Two-Pass Model

Dekai Wu,Pascale Fung

Published 2009 in North American Chapter of the Association for Computational Linguistics

ABSTRACT

We present results on a novel hybrid semantic SMT model that incorporates the strengths of both semantic role labeling and phrase-based statistical machine translation. The approach avoids major complexity limitations via a two-pass architecture. The first pass is performed using a conventional phrase-based SMT model. The second pass is performed by a re-ordering strategy guided by shallow semantic parsers that produce both semantic frame and role labels. Evaluation on a Wall Street Journal newswire genre test set showed the hybrid model to yield an improvement of roughly half a point in BLEU score over a strong pure phrase-based SMT baseline -- to our knowledge, the first successful application of semantic role labeling to SMT.

PUBLICATION RECORD

  • Publication year

    2009

  • Venue

    North American Chapter of the Association for Computational Linguistics

  • Publication date

    2009-05-31

  • Fields of study

    Computer Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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