The key challenge of multi-domain translation lies in simultaneously encoding
both the general knowledge shared across domains and the particular knowledge
distinctive to each domain in a unified model. Previous work shows that the
standard neural machine translation (NMT) model, trained on mixed-domain data,
generally captures the general knowledge, but misses the domain-specific
knowledge. In response to this problem, we augment NMT model with additional
domain transformation networks to transform the general representations to
domain-specific representations, which are subsequently fed to the NMT decoder.
To guarantee the knowledge transformation, we also propose two complementary
supervision signals by leveraging the power of knowledge distillation and
adversarial learning. Experimental results on several language pairs, covering
both balanced and unbalanced multi-domain translation, demonstrate the
effectiveness and universality of the proposed approach. Encouragingly, the
proposed unified model achieves comparable results with the fine-tuning
approach that requires multiple models to preserve the particular knowledge.
Further analyses reveal that the domain transformation networks successfully
capture the domain-specific knowledge as expected.

本篇论文提出一种利用领域转换网络、知识蒸馏和对抗学习等方法，解决多领域翻译中通用和特定知识的统一建模问题，并在多种语言对上得到了比 fine-tuning 方法更为优秀的结果。