Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging

• URL: http://arxiv.org/abs/2102.10160v1
• Date: Fri, 19 Feb 2021 21:19:42 GMT
• Title: Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging
• Authors: Emmanouil Stergiadis, Satendra Kumar, Fedor Kovalev, Pavel Levin
• Abstract summary: We describe and empirically evaluate multidimensional tagging (MDT), a simple yet effective method for passing sentence-level information to the model. Our human and BLEU evaluation results show that MDT can be applied to the problem of multi-domain adaptation and significantly reduce training costs without sacrificing the translation quality on any of the constituent domains.
• Score: 1.433758865948252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many modern Neural Machine Translation (NMT) systems are trained on nonhomogeneous datasets with several distinct dimensions of variation (e.g. domain, source, generation method, style, etc.). We describe and empirically evaluate multidimensional tagging (MDT), a simple yet effective method for passing sentence-level information to the model. Our human and BLEU evaluation results show that MDT can be applied to the problem of multi-domain adaptation and significantly reduce training costs without sacrificing the translation quality on any of the constituent domains.

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