Dependency Treelet Translation: Syntactically Informed Phrasal SMT 
 We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation . This method requires a source-language dependency parser , target language word segmentation and an unsupervised word alignment component . We align a parallel corpus , project the source dependency parse onto the target sentence , extract dependency treelet translation pairs , and train a tree-based ordering model . We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser . 