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

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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 1.53 TER on average. Results of comparative study with other seven widely used reordering models will also be reported.

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