To deal with the long-distance reordering

a linguistically syntax-based reordering model was presented for phrasal statistical machine translation. In this model

the syntax structure was decomposed according to the phrase segmentation to avoid the inconsistence between phrase and syntax. The reordering sequence of the sub-structures of a parse tree was decided by the word and phrase alignments. The reordering probability of the sub-structure was calculated on the reordering probabilities of the inside nodes

which was defined as a feature function of the log-linear statistical translation model. Experimental results show that the BLEU scores of the translation results were significantly improved compared with a conventional statistical phrase-based model. Therefore

it is effective to introduce the linguistic syntax for phrase reordering. The presented reordering model is able to efficiently incorporate the syntax into the translation process of phrases.