POSbased reordering models for statistical machine translation

We present a novel word reordering model for phrase-based statistical machine translation suited to cope with long-span word move-ments. In particular, reordering of nouns, verbs and adjectives is modeled by taking into account target-to-source word alignments and the distances between source as well as target words. The proposed model was applied as a set of additional feature functions to re-score N-best translation candidates generated by a statistical machine translation system featuring state-of-the-art lexicalized reordering mod-els. Experiments showed relative BLEU score improvement up to 7.3 % on the BTEC Japanese-to-English task, and up to 1.1 % on the Europarl German-to-English task. 1