Phrase Reordering Model Integrating Syntactic Knowledge for SMT

Reordering model is important for the statistical machine translation (SMT). Current phrase-based SMT technologies are good at capturing local reordering but not global reordering. This paper introduces syntactic knowledge to improve global reordering capability of SMT system. Syntactic knowledge such as boundary words, POS information and dependencies is used to guide phrase reordering. Not only constraints in syntax tree are proposed to avoid the reordering errors, but also the modification of syntax tree is made to strengthen the capability of capturing phrase reordering. Furthermore, the combination of parse trees can compensate for the reordering errors caused by single parse tree. Finally, experimental results show that the performance of our system is superior to that of the state-of-the-art phrase-based SMT system.