A neural reordering model based on phrasal dependency tree for statistical machine translation

Machine translation is an important field of research and development. Word reordering is one of the main problems in machine translation. It is an important factor of quality and efficiency of machine translations and becomes more difficult when it deals with structurally divergent language pairs. To overcome this problem, we introduce a neural reordering model, using phrasal dependency trees which depict dependency relations among contiguous non-syntactic phrases. The model makes the use of reordering rules, which are automatically learned by a probabilistic neural network classifier from a reordered phrasal dependency tree bank. The proposed model combines the power of the lexical reordering and syntactic pre-ordering models by performing long-distance reorderings. The proposed reordering model is integrated into a standard phrase-based statistical machine translation system to translate input sentences. Our method is evaluated on syntactically divergent language-pairs, English -> Persian and English -> German using WMT07 benchmark. The results illustrate the superiority of the proposed method in terms of BLEU, TER and LRscore on both translation tasks. On average the proposed method retrieves a significant impact on precision and recall values respect to the hierarchical, lexicalized and distortion reordering models.