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. model have been reported in (Koehn et al., 2005). However, the amount of the training data for each bilingual phrase is so small that the model usually suffers from the data sparseness problem. .
