Multi-Domain Adaptation for SMT Using Multi-Task Learning

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 approach
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 parameters
of these models are tuned jointly via
MTL so that they can learn general knowledge
more accurately and exploit domain knowledge
better. Our experiments on a largescale
English-to-Chinese translation task validate
that the MTL-based adaptation approach
significantly and consistently improves the
translation quality compared to a non-adapted
baseline. Furthermore, it also outperforms the
individual adaptation of each specific domain.