Zahra Rahimi
Shahram Khadivi
Heshaam Faili

We propose three novel reordering models for statistical machine translation. These reordering models use dependency tree to improve the translation quality. All reordering models are utilized as features in a log linear framework and therefore guide the decoder to make better decisions about reordering. These reordering models are tested on two English-Persian parallel corpora with different statistics and domains. The BLEU score is improved by ۲.۵ on the first corpus and by ۱.۲ on the other.

statistical machine translation, reordering model, dependency tree, discriminative reordering model, discriminative decoder, long range reordering, maximum entropy, distortion model