Paper: Adapting Translation Models to Translationese Improves SMT

ACL ID E12-1026
Title Adapting Translation Models to Translationese Improves SMT
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Translation models used for statistical ma- chine translation are compiled from par- allel corpora; such corpora are manually translated, but the direction of translation is usually unknown, and is consequently ig- nored. However, much research in Trans- lation Studies indicates that the direction of translation matters, as translated language (translationese) has many unique proper- ties. Specifically, phrase tables constructed from parallel corpora translated in the same direction as the translation task perform better than ones constructed from corpora translated in the opposite direction. We reconfirm that this is indeed the case, but emphasize the importance of using also texts translated in the ?wrong? direction. We take advantage of information pertain- ing to the direction of trans...