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A Survey of Domain Adaptation for Neural Machine Translation

by   Chenhui Chu, et al.
Osaka University
National Institute of Information and Communications Technology

Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality and domain-specific translation is crucial in the real world, domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as monolingual corpora for in-domain translation, is very important for domain-specific translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for NMT.


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An Empirical Study of Domain Adaptation for Unsupervised Neural Machine Translation

Domain adaptation methods have been well-studied in supervised neural ma...

Iterative Dual Domain Adaptation for Neural Machine Translation

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