Topic models for dynamic translation model adaptation

  • Authors:

  • Vladimir Eidelman;Jordan Boyd-Graber;Philip Resnik

  • Affiliations:

  • University of Maryland, College Park, MD;University of Maryland, College Park, MD;University of Maryland, College Park, MD

  • Venue:
  • ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
  • Year:
  • 2012

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Abstract

We propose an approach that biases machine translation systems toward relevant translations based on topic-specific contexts, where topics are induced in an unsupervised way using topic models; this can be thought of as inducing subcorpora for adaptation without any human annotation. We use these topic distributions to compute topic-dependent lexical weighting probabilities and directly incorporate them into our translation model as features. Conditioning lexical probabilities on the topic biases translations toward topic-relevant output, resulting in significant improvements of up to 1 BLEU and 3 TER on Chinese to English translation over a strong baseline.