Specializing Multi-domain NMT via Penalizing Low Mutual Information
Jiyoung Lee, Hantae Kim, Hyunchang Cho, Edward Choi, Cheonbok Park · 2022
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-theart 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 multidomain NMT.