Paper: Multi-Domain Adaptation for SMT Using Multi-Task Learning

ACL ID D13-1107
Title Multi-Domain Adaptation for SMT Using Multi-Task Learning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Domain adaptation for SMT usually adapts models to an individual specific domain. However, it often lacks some correlation among different domains where common knowledge could be shared to improve the overall translation quality. In this paper, we propose a novel multi-domain adaptation ap- proach for SMT using Multi-Task Learning (MTL), with in-domain models tailored for each specific domain and a general-domain model shared by different domains. The pa- rameters of these models are tuned jointly via MTL so that they can learn general knowledge more accurately and exploit domain knowl- edge better. Our experiments on a large- scale English-to-Chinese translation task val- idate that the MTL-based adaptation approach significantly and consistently improves the translation quality compared ...