Computer Science > Computation and Language
arXiv:2210.12910 (cs)
[Submitted on 24 Oct 2022]
Title:Specializing Multi-domain NMT via Penalizing Low Mutual Information
View a PDF of the paper titled Specializing Multi-domain NMT via Penalizing Low Mutual Information, by Jiyoung Lee and 4 other authors
View PDFAbstract: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.
| Comments: | Accepted in EMNLP 2022 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2210.12910 [cs.CL] |
| (orarXiv:2210.12910v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2210.12910 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Specializing Multi-domain NMT via Penalizing Low Mutual Information, by Jiyoung Lee and 4 other authors
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