Beyond left-to-right: Multiple decomposition structures for smt

Standard phrase-based translation models do not explicitly model context dependence be-tween translation units. As a result, they rely on large phrase pairs and target language mod-els to recover contextual effects in translation. In this work, we explore n-gram models over Minimal Translation Units (MTUs) to explic-itly capture contextual dependencies across phrase boundaries in the channel model. As there is no single best direction in which con-textual information should flow, we explore multiple decomposition structures as well as dynamic bidirectional decomposition. The resulting models are evaluated in an intrin-sic task of lexical selection for MT as well as a full MT system, through n-best rerank-ing. These experiments demonstrate that ad-ditional contextual modeling does indeed ben-efit a phrase-based system and that the direc-tion of conditioning is important. Integrating multiple conditioning orders provides consis-tent benefit, and the most important directions differ by language pair.