A Survey of Domain Adaptation for Machine Translation
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- Chu Chenhui
- Institute for Datability Science, Osaka University
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- Wang Rui
- National Institute of Information and Communications Technology
Abstract
<p>Neural machine translation (NMT) is a deep learning based approach for machine translation, which outperforms traditional statistical machine translation (SMT) and yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although a 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 MT. Because of the current dominance of NMT in MT research, we give a brief review of domain adaptation for SMT, but put most of our effort into the survey of domain adaptation for NMT. We hope that this paper will be both a starting point and a source of new ideas for researchers and engineers who are interested in domain adaptation for MT.</p>
Journal
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- Journal of Information Processing
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Journal of Information Processing 28 (0), 413-426, 2020
Information Processing Society of Japan
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Details 詳細情報について
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- CRID
- 1390003825205780480
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- NII Article ID
- 130007887723
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- ISSN
- 18826652
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- Text Lang
- en
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed