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arXiv · 2210.12910

Specializing Multi-domain NMT via Penalizing Low Mutual Information

Jiyoung Lee·Hantae Kim·Hyunchang Cho·Edward Choi·Cheonbok Park
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Abstract

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.

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Jiyoung Lee, Hantae Kim, Hyunchang Cho, Edward Choi, Cheonbok Park. 2022-10-24. Specializing Multi-domain NMT via Penalizing Low Mutual Information. https://arxiv.org/abs/2210.12910

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