Syntax- and semantic-based reordering in hierarchical phrase-based statistical machine translation

We evaluate our RM both intrinsically (accuracy of the RM classifier) and extrinsically (MT). Our best configuration outperforms the baseline classifier by 5-29% on pairs of dependants and by 12-30% on head and dependant pairs while the improvement on MT ranges between 1.6% and 5.5% relative in terms of BLEU depending on language pair and domain. We also analyze the value of the feature weights to obtain further insights on the impact of the reordering-related features in the HPB-SMT model. We observe that the features of our RM are assigned significant weights and that our features are complementary to the reordering feature included by default in the HPB-SMT model.