The authors describe a novel approach to MT that combines the strengths of the two leading corpus-based approaches: phrasal SMT and EBMT. They use a syntactically informed decoder and reordering model based on the source dependency tree, in combination with conventional SMT models to incorporate the power of phrasal SMT with the linguistic generality available in a parser. They show that this approach significantly outperforms a leading string-based Phrasal SMT decoder and an EBMT system. They present results from two radically different language pairs, and investigate the sensitivity of this approach to parse quality by using two distinct parsers and oracle experiments. They also validate our automated bleu scores with a small human evaluation.