Bayesian adaptation for statistical machine translation

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. © 2010 Springer-Verlag Berlin Heidelberg.