Neural networks are known to be data hungry and domain sensitive, but it is
nearly impossible to obtain large quantities of labeled data for every domain
we are interested in. This necessitates the use of domain adaptation
strategies. One common strategy encourages generalization by aligning the
global distribution statistics between source and target domains, but one
drawback is that the statistics of different domains or tasks are inherently
divergent, and smoothing over these differences can lead to sub-optimal
performance. In this paper, we propose the framework of {\it Domain
Differential Adaptation (DDA)}, where instead of smoothing over these
differences we embrace them, directly modeling the difference between domains
using models in a related task. We then use these learned domain differentials
to adapt models for the target task accordingly. Experimental results on domain
adaptation for neural machine translation demonstrate the effectiveness of this
strategy, achieving consistent improvements over other alternative adaptation
strategies in multiple experimental settings.

本文介绍了『Domain Differential Adaptation（DDA）』的框架，该框架通过使用相关任务中的模型直接建模不同领域之间的差异，从而成功地应用于神经机器翻译的领域自适应中，并在多个实验设置中实现比其他替代方法更好的改进效果。