Understand
Machine translation systems are very sensitive to the domains they were trained on.
- Several domain adaptation techniques have been deeply studied.
- We propose a new technique for neural machine translation (NMT) that we call domain control which is performed at runtime using a unique neural network covering multiple domains.
- The presented approach shows quality improvements when compared to dedicated domains translating on any of the covered domains and even on out-of-domain data.
Built on
Adaptation of the translation model for statistical machine translation based on information retrieval
Almut Silja Hildebrand, Matthias Eck, Stephan Vogel, and Alex Waibel. 2005 · 2005
Earlier work this paper cites.
Selecting relevant text subsets from web-data for building topic specific language models
Abhinav Sethy, Panayiotis Georgiou, and Shrikanth Narayanan. 2006 · 2006
Earlier work this paper cites.
Mixture-model adaptation for SMT
George Foster and Roland Kuhn. 2007 · 2007
Earlier work this paper cites.
Experiments in domain adaptation for statistical machine translation
Philipp Koehn and Josh Schroeder. 2007 · 2007
Earlier work this paper cites.
Large and diverse language models for statistical machine translation
Holger Schwenk and Philipp Koehn. 2008 · 2008
Earlier work this paper cites.
Intelligent selection of language model training data
Robert C. Moore and William Lewis. 2010 · 2010
Earlier work this paper cites.
Similar
Parallel data, tools and interfaces in opus
Jörg Tiedemann. 2012 · 2012
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014 · 2014
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Stanford neural machine translation systems for spoken language domains
Thang Luong and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 2015 · 2015
Cited alongside, same era.
Then
Guided alignment training for topic-aware neural machine translation
Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016 · 2016
Closest in time.
Systran’s pure neural machine translation systems
Josep Crego, Jungi Kim, Guillaume Klein, Anabel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean Lorieux, Leidiana Martins, Dang-Chuan Nguyen, Alexandra Priori, Thomas Riccardi, Natalia Segal, Christophe Servan, Cyril Tiquet, Bo Wang, Jin Yang, Dakun Zhang, Jing Zhou, and Peter Zoldan. 2016 · 2016
Closest in time.
Melvin Johnson, Mike Schuster, Quoc V.Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, and Nikkik Thorat. 2016 · 2016
Closest in time.
Controlling politeness in neural machine translation via side constraints
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Closest in time.
Topic-informed neural machine translation
Jian Zhang, Liangyou Li, Andy Way, and Qun Liu. 2016 · 2016
Closest in time.
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