  Multi-domain Neural Machine Translation (NMT) trains a single model with
multiple domains. It is appealing because of its efficacy in handling multiple
domains within one model. An ideal multi-domain NMT should learn distinctive
domain characteristics simultaneously, however, grasping the domain peculiarity
is a non-trivial task. In this paper, we investigate domain-specific
information through the lens of mutual information (MI) and propose a new
objective that penalizes low MI to become higher. Our method achieved the
state-of-the-art performance among the current competitive multi-domain NMT
models. Also, we empirically show our objective promotes low MI to be higher
resulting in domain-specialized multi-domain NMT.
