Paper: Advancements in Reordering Models for Statistical Machine Translation

ACL ID P13-1032
Title Advancements in Reordering Models for Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

In this paper, we propose a novel re- ordering model based on sequence label- ing techniques. Our model converts the reordering problem into a sequence label- ing problem, i.e. a tagging task. Results on five Chinese-English NIST tasks show that our model improves the baseline sys- tem by 1.32 BLEU and 1.53 TER on av- erage. Results of comparative study with other seven widely used reordering mod- els will also be reported.