Advancements in Reordering Models for Statistical Machine Translation
Minwei Feng, Jan-Thorsten Peter and Hermann Ney
The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013
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 1.53 TER on average. Results of comparative study with other seven widely used reordering models will also be reported.
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