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Neural Machine Translation (NMT) models are sensitive to small perturbations in the input.
Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Minh-Thang Luong, and Quoc V. Le. 2019 · 1904
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Bpe-dropout: Simple and effective subword regularization
Ivan Provilkov, Dmitrii Emelianenko, and Elena Voita. 2019 · 1910
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Improving robustness of machine translation with synthetic noise
Vaibhav Vaibhav, Sumeet Singh, Craig Stewart, and Graham Neubig. 2019 · 1920
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
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Europarl: A parallel corpus for statistical machine translation
Philipp Koehn. 2005 · 2005
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A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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OpenSubtitles2016: Extracting large parallel corpora from movie and TV subtitles
Pierre Lison and Jörg Tiedemann. 2016 · 2016
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Practical black-box attacks against deep learning systems using adversarial examples
Nicolas Papernot, Patrick D. McDaniel, Ian J. Goodfellow, Somesh Jha, Z. Berkay Celik, and Ananthram Swami. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Sockeye: A toolkit for neural machine translation
Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton, and Matt Post. 2017 · 2017
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Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2017
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Toward robust neural machine translation for noisy input sequences
Matthias Sperber, Jan Niehues, and Alex Waibel. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Synthetic and natural noise both break neural machine translation
Subword regularization: Improving neural network translation models with multiple subword candidates
Taku Kudo. 2018 · 2018
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MTNT: A testbed for machine translation of noisy text
Paul Michel and Graham Neubig. 2018 · 2018
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A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
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Machine translation of restaurant reviews: New corpus for domain adaptation and robustness
Alexandre Berard, Ioan Calapodescu, Marc Dymetman, Claude Roux, Jean-Luc Meunier, and Vassilina Nikoulina. 2019 · 2019
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Robust neural machine translation with doubly adversarial inputs
Yong Cheng, Lu Jiang, and Wolfgang Macherey. 2019 · 2019
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Training on synthetic noise improves robustness to natural noise in machine translation
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Yonatan Belinkov and Yonatan Bisk. 2018 · 2018
Cited alongside, same era.
Findings of the 2018 conference on machine translation (WMT18)
Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, and Christof Monz. 2018 · 2018
Cited alongside, same era.
Towards robust neural machine translation
Yong Cheng, Zhaopeng Tu, Fandong Meng, Junjie Zhai, and Yang Liu. 2018 · 2018
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On adversarial examples for character-level neural machine translation
Javid Ebrahimi, Daniel Lowd, and Dejing Dou. 2018 · 2018
Cited alongside, same era.
Vladimir Karpukhin, Omer Levy, Jacob Eisenstein, and Marjan Ghazvininejad. 2019 · 2019
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Findings of the first shared task on machine translation robustness
Xian Li, Paul Michel, Antonios Anastasopoulos, Yonatan Belinkov, Nadir Durrani, Orhan Firat, Philipp Koehn, Graham Neubig, Juan Pino, and Hassan Sajjad. 2019 · 2019
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Robust neural machine translation with joint textual and phonetic embedding
Hairong Liu, Mingbo Ma, Liang Huang, Hao Xiong, and Zhongjun He. 2019 · 2019
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On evaluation of adversarial perturbations for sequence-to-sequence models
Paul Michel, Xian Li, Graham Neubig, and Juan Pino. 2019 · 2019
Later among the works it cites.