Bayesian Adaptation for Statistical Machine Translation

  • Germán Sanchis-Trilles
  • Francisco Casacuberta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

In many pattern recognition problems, learning from training samples is a process that requires important amounts of training data and a high computational effort. Sometimes, only limited training data and/or limited computational resources are available, but there is also available a previous system trained for a closely related task and with enough training material. This scenario is very frequent in statistical machine translation and adaptation can be a solution to deal with this problem. In this paper, we present an adaptation technique for (state-of-the-art) log-linear modelling based on the well-known Bayesian learning paradigm. This technique has been applied to statistical machine translation and can be easily extended to other pattern recognition areas in which log-linear models are used. We show empirical results in which a small amount of adaptation data is able to improve both the non-adapted system and a system that optimises the above-mentioned weights only on the adaptation set.

Keywords

Machine Translation Adaptation Data Statistical Machine Translation Bayesian Learning Parallel Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Germán Sanchis-Trilles
    • 1
  • Francisco Casacuberta
    • 1
  1. 1.Instituto Tecnológico de Informática, Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de Valencia 

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