Semantic roles for SMT: a hybrid two-pass model

  • Authors:
  • Dekai Wu;Pascale Fung

  • Affiliations:
  • University of Science and Technology, Clear Water Bay, Hong Kong;University of Science and Technology, Clear Water Bay, Hong Kong

  • Venue:
  • NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
  • Year:
  • 2009

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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.