Novel reordering approaches in phrase-based statistical machine translation

This paper presents novel approaches to reordering in phrase-based statistical machine
translation. We perform consistent reordering of source sentences in training and estimate a
statistical translation model. Using this model, we follow a phrase-based monotonic machine
translation approach, for which we develop an efficient and flexible reordering framework
that allows to easily introduce different reordering constraints. In translation, we apply source
sentence reordering on word level and use a reordering automaton as input. 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
improves translation quality.