M.: Phrase reordering model integrating syntactic knowledge for SMT. In: EMNLP/CoNLL

Reordering model is important for the sta-tistical 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 know-ledge such as boundary words, POS infor-mation and dependencies is used to guide phrase reordering. Not only constraints in syntax tree are proposed to avoid the reor-dering errors, but also the modification of syntax tree is made to strengthen the capa-bility of capturing phrase reordering. Fur-thermore, the combination of parse trees can compensate for the reordering errors caused by single parse tree. Finally, expe-rimental results show that the performance of our system is superior to that of the state-of-the-art phrase-based SMT system.