Multi-Domain Neural Machine Translation

Abstract

We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use multilingual NMT methods to create multi-domain translation systems, we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary, turns out that it is after all, but also that when it is not known quite high translation quality can be reached.

Accepted to EAMT'2018, In Proceedings of the 21st Annual Conference of the European Association for Machine Translation (EAMT'2018)

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• Neural Machine Translation: A Review and Survey
• Extremely low-resource machine translation for closely related languages
• Iterative Dual Domain Adaptation for Neural Machine Translation
• Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training