Paper: Phrase Reordering Model Integrating Syntactic Knowledge for SMT

ACL ID D07-1056
Title Phrase Reordering Model Integrating Syntactic Knowledge for SMT
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2007
Authors

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 ...