We present a novel word reordering model for phrase-based statistical machine translation suited to cope with long-span word movements.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 models. 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
Dr. Deepa Gupta, Cettolo, M., and Federico, M., “POS-based reordering models for statistical machine translation”, Proceedings of the Machine Translation Summit (MT-Summit), 2007.