We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to capture phrase reorderings using a structure learning framework. On both the reordering classiﬁcation and a Chinese-to-English translation task, we show improved performance over a baseline SMT system.
|Translated title of the contribution||Handling phrase reordering for machine translation|
|Title of host publication||the joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Singapore|
|Pages||241 - 244|
|Number of pages||4|
|Publication status||Published - 2009|