A Survey of Domain Adaptation for Neural Machine Translation
A Survey of Domain Adaptation for Neural Machine Translation
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发表时间：
2018-06
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通讯作者：
Chenhui Chu;Rui Wang
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作者：
Chenhui Chu;Rui Wang
神经机器翻译（NMT）是一种基于深度学习的机器翻译方法，它可以在大规模平行语料库可用的情况下产生最先进的翻译性能。尽管高质量和特定领域的翻译在现实世界中是至关重要的，但特定领域的语料库通常很少或不存在，因此香草NMT在这种情况下表现不佳。利用领域外平行语料库和单语语料库进行领域内翻译的领域自适应对于特定领域的翻译非常重要。本文对NMT领域自适应技术进行了综述。
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.