Context-dependent alignment models for statistical machine translation

  • Authors:
  • Jamie Brunning;Adrià de Gispert;William Byrne

  • Affiliations:
  • Cambridge University, Cambridge, U.K.;Cambridge University, Cambridge, U.K.;Cambridge University, Cambridge, U.K.

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

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Abstract

We introduce alignment models for Machine Translation that take into account the context of a source word when determining its translation. Since the use of these contexts alone causes data sparsity problems, we develop a decision tree algorithm for clustering the contexts based on optimisation of the EM auxiliary function. We show that our context-dependent models lead to an improvement in alignment quality, and an increase in translation quality when the alignments are used in Arabic-English and Chinese-English translation.