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A Survey Of Domain Adaptation For Neural Machine Translation

Chenhui Chu, Rui Wang . Arxiv 2018 – 137 citations

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Compositional Generalization Content Enrichment Fine Tuning Interdisciplinary Approaches Neural Machine Translation Survey Paper Variational Autoencoders Visual Question Answering

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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