The joint probability model proposed by Marcu and Wong (2002) provides a strong probabilistic framework for phrase-based statistical machine translation (SMT). The model's usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present the first model to use word alignments for constraining the space of phrasal alignments searched during Expectation Maximization (EM) training. Constraining the joint model improves performance, showing results that are very close to stateof -the-art phrase-based models. It also allows it to scale up to larger corpora and therefore be more widely applicable.

The Microsoft Research translation system is a syntactically informed phrasal SMT system that uses a phrase translation model based on dependency treelets and a global reordering model based on the source dependency tree. These models are combined with several other knowledge sources in a log-linear manner. The weights of the individual components in the loglinear model are set by an automatic parametertuning method. We give a brief overview of the components of the system and discuss our experience with the Europarl data translating from English to Spanish.

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