Log-linear weight optimisation via Bayesian Adaptation
in Statistical Machine Translation
In this paper, we present an adaptation technique for statistical machine translation, in which we apply the well-known Bayesian adaptation paradigm with the purpose of 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.