An efficient two-pass approach to synchronous-CFG driven statistical MT

Ashish Venugopal, Andreas Zollmann, Stephan Vogel

Research output: Contribution to conferencePaper

Abstract

We present an efficient, novel two-pass approach to mitigate the computational impact resulting from online intersection of an n-gram language model (LM) and a probabilistic synchronous context-free grammar (PSCFG) for statistical machine translation. In first pass CYK-style decoding, we consider first-best chart item approximations, generating a hypergraph of sentence spanning target language derivations. In the second stage, we instantiate specific alternative derivations from this hypergraph, using the LM to drive this search process, recovering from search errors made in the first pass. Model search errors in our approach are comparable to those made by the state-of-the-art "Cube Pruning" approach in (Chiang, 2007) under comparable pruning conditions evaluated on both hierarchical and syntax-based grammars.

Original languageEnglish
Pages500-507
Number of pages8
Publication statusPublished - 1 Dec 2007
EventHuman Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007 - Rochester, NY, United States
Duration: 22 Apr 200727 Apr 2007

Other

OtherHuman Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007
CountryUnited States
CityRochester, NY
Period22/4/0727/4/07

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ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

Cite this

Venugopal, A., Zollmann, A., & Vogel, S. (2007). An efficient two-pass approach to synchronous-CFG driven statistical MT. 500-507. Paper presented at Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2007, Rochester, NY, United States.