Advancements in Reordering Models for Statistical Machine Translation

Minwei Feng, Jan-Thorsten Peter, Hermann Ney

Abstract: In this paper, we propose a novel reordering model based on sequence labeling techniques. Our model converts the reordering problem into a sequence labeling problem, i.e. a tagging task. Results on five Chinese-English NIST tasks show that our model improves the baseline system by 1.32 BLEU and 1.53 TER on average. Results of comparative study with other seven widely used reordering models will also be reported.