Handling phrase reorderings for machine translation

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan

Abstract: 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.