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Optimization of Log-linear Machine Translation Model Parameters Using SVMs. Proceedings of the 8th International Workshop on Pattern Recognition in Information Systems (PRIS 2008), 2008.The state-of-the art in statistical machine translation is based on a log-linear combination of different models. In this approach, the coefficients of the combination are computed by using the MERT algorithm with a validation data set. This algorithm presents high computational costs. As an alternative, we propose a novel technique based on Support Vector Machines to calculate these coefficients using a loss function to be minimized. We report the experiments on a Italian-English translation task showing encouraging results.