Mixing multiple translation models in statistical machine translation

  • Authors:
  • Majid Razmara;George Foster;Baskaran Sankaran;Anoop Sarkar

  • Affiliations:
  • Simon Fraser University, Burnaby, BC, Canada;National Research Council Canada, Gatineau, QC, Canada;Simon Fraser University, Burnaby, BC, Canada;Simon Fraser University, Burnaby, BC, Canada

  • Venue:
  • ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
  • Year:
  • 2012

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Abstract

Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain. We propose a novel approach, ensemble decoding, which combines a number of translation systems dynamically at the decoding step. In this paper, we evaluate performance on a domain adaptation setting where we translate sentences from the medical domain. Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation.