Optimization of Log-linear Machine Translation Model Parameters Using SVMs

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.