A Novel Reordering Model for Statistical Machine Translation

Word reordering is one of the fundamental problems of machine translation, and an important factor of its quality and efficiency. In this paper, we introduce a novel reordering model based on an innovative structure, named, phrasal dependency tree including syntactical and statistical information in context of a log-linear model. The phrasal dependency tree is a new modern syntactic structure based on dependency relations between contiguous nonsyntactic phrases. In comparison with well-known and popular reordering models such as the distortion, lexicalized and hierarchical models, the experimental study demonstrates the superiority of our model regarding to the different evaluation measures. We evaluated the proposed model on a PersianEnglish SMT system. On average our model retrieved a significant impact on precision with comparable recall value respect to the lexicalized and distortion models, and is found to be effective for medium and long-distance reordering.