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Translating text that diverges from the training domain is a key challenge for machine translation.
BLEU: A Method for Automatic Evaluation of Machine Translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J. (2002) · 2002
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Moses: Open Source Toolkit for Statistical Machine Translation
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., and Herbst, E. (2007) · 2007
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The Trilingual ALLEGRA Corpus: Presentation and Possible Use for Lexicon Induction
Scherrer, Y. and Cartoni, B. (2012) · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013) · 2013
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Do deep nets really need to be deep?
Ba, J. and Caruana, R. (2014) · 2014
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Explaining and harnessing adversarial examples (2014)
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014) · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C. (2015) · 2015
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
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Stanford Neural Machine Translation Systems for Spoken Language Domains
Luong, M.-T. and Manning, C. D. (2015) · 2015
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Incorporating discrete translation lexicons into neural machine translation
Arthur, P., Neubig, G., and Nakamura, S. (2016) · 2016
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Sequence-level knowledge distillation
Kim, Y. and Rush, A. M. (2016) · 2016
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Mutual information and diverse decoding improve neural machine translation
Li, J. and Jurafsky, D. (2016) · 2016
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OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles
Lison, P. and Tiedemann, J. (2016) · 2016
Cited alongside, same era.
Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A. (2016) · 2016
Cited alongside, same era.
Improving Neural Machine Translation Models with Monolingual Data
Sennrich, R., Haddow, B., and Birch, A. (2016a) · 2016
Cited alongside, same era.
Neural Machine Translation of Rare Words with Subword Units
Sennrich, R., Haddow, B., and Birch, A. (2016b) · 2016
Cited alongside, same era.
Categorical reparametrization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2017) · 2017
Cited alongside, same era.
Google’s multilingual neural machine translation system: Enabling zero-shot translation
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F., Wattenberg, M., Corrado, G., Hughes, M., and Dean, J. (2017) · 2017
Synthetic and natural noise both break neural machine translation
Belinkov, Y. and Bisk, Y. (2018) · 2018
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The Sockeye Neural Machine Translation Toolkit at AMTA 2018
Hieber, F., Domhan, T., Denkowski, M., Vilar, D., Sokolov, A., Clifton, A., and Post, M. (2018) · 2018
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Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
Kudo, T. (2018) · 2018
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mtrain: A convenience tool for machine translation
Läubli, S., Müller, M., Horat, B., and Volk, M. (2018) · 2018
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Hallucinations in neural machine translation
Lee, K., Firat, O., Agarwal, A., Fannjiang, C., and Sussillo, D. (2018) · 2018
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Improving Lexical Choice in Neural Machine Translation
Nguyen, T. and Chiang, D. (2018) · 2018
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Cited alongside, same era.
Domain control for neural machine translation
Kobus, C., Crego, J., and Senellart, J. (2017) · 2017
Cited alongside, same era.
Six Challenges for Neural Machine Translation
Koehn, P. and Knowles, R. (2017) · 2017
Cited alongside, same era.
Neural Machine Translation with Reconstruction
Tu, Z., Liu, Y., Shang, L., Liu, X., and Li, H. (2017) · 2017
Cited alongside, same era.
Attention is All you Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Cited alongside, same era.
The neural noisy channel
Yu, L., Blunsom, P., Dyer, C., Grefenstette, E., and Kociský, T. (2017) · 2017
Cited alongside, same era.
Later among the works it cites.
A call for clarity in reporting bleu scores
Post, M. (2018) · 2018
Later among the works it cites.
Findings of the First Shared Task on Machine Translation Robustness
Li, X., Michel, P., Anastasopoulos, A., Belinkov, Y., Durrani, N., Firat, O., Koehn, P., Neubig, G., Pino, J., and Sajjad, H. (2019) · 2019
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Bi-directional differentiable input reconstruction for low-resource neural machine translation
Niu, X., Xu, W., and Carpuat, M. (2019) · 2019
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fairseq: A fast, extensible toolkit for sequence modeling
Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., and Auli, M. (2019) · 2019
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Simple and effective noisy channel modeling for neural machine translation
Yee, K., Dauphin, Y., and Auli, M. (2019) · 2019
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