Using Dependency Tree Grammar to Enhance the Reordering Model of Statistical Machine Translation Systems

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 2.5 on the first corpus and by 1.2 on the other.