Novel reordering approaches in phrase-based statistical machine translation

This paper presents novel approaches to reordering in phrase-based statistical ma-chine translation. We perform consistent reordering of source sentences in train-ing and estimate a statistical translation model. Using this model, we follow a phrase-based monotonic machine transla-tion approach, for which we develop an ef-ficient and flexible reordering framework that allows to easily introduce different re-ordering constraints. In translation, we apply source sentence reordering on word level and use a reordering automaton as in-put. We show how to compute reordering automata on-demand using IBM or ITG constraints, and also introduce two new types of reordering constraints. We further add weights to the reordering automata. We present detailed experimental results and show that reordering significantly im-proves translation quality.