Log-linear weight optimisation via Bayesian adaptation in statistical machine translation

  • Authors:
  • Germán Sanchis-Trilles;Francisco Casacuberta

  • Affiliations:
  • Universidad Politécnica de Valencia;Universidad Politécnica de Valencia

  • Venue:
  • COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
  • Year:
  • 2010

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Abstract

We present an adaptation technique for statistical machine translation, which applies the well-known Bayesian learning paradigm for adapting the model parameters. Since state-of-the-art statistical machine translation systems model the translation process as a log-linear combination of simpler models, we present the formal derivation of how to apply such paradigm to the weights of the log-linear combination. We show empirical results in which a small amount of adaptation data is able to improve both the non-adapted system and a system which optimises the above-mentioned weights on the adaptation set only, while gaining both in reliability and speed.