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Log-linear weight optimisation via Bayesian Adaptation in Statistical Machine Translation. Proceedings of 23rd International Conference on Computational Linguistics (COLING2010), 2010. pp. 1077-1085. http://aclweb.org/anthology-new/C/C10/C10-2124.pdfIn 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.