A Survey of Domain Adaptation for Neural Machine Translation
This is a survey paper, so it is incremental and provides a comprehensive overview for researchers in machine translation.
The paper surveys domain adaptation techniques for neural machine translation to address poor performance when domain-specific parallel corpora are scarce, summarizing state-of-the-art methods without presenting new results.
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.