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Neural machine translation (NMT) systems have been shown to give undesirable translation when a small change is made in the source sentence.
K. Papineni, S. Roukos, T. Ward, and W.-J. Zhu, “Bleu: a method for automatic evaluation of machine translation,” in Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics , 2002
2002
Earlier work this paper cites.
T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2015, pp. 1412–1421
2015
Earlier work this paper cites.
M. R. Costa-jussà and J. A. R. Fonollosa, “Character-based neural machine translation,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 357–361
2016
Earlier work this paper cites.
R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Berlin, Germany: Association for Computational Linguistics, Aug. 2016, pp. 1715–1725
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30 , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds. Curran Associates, Inc., 2017, pp. 5998–6008
2017
Earlier work this paper cites.
S. Feng, E. Wallace, A. Grissom II, M. Iyyer, P. Rodriguez, and J. Boyd-Graber, “Pathologies of neural models make interpretations difficult,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2018, pp. 3719–3728
2018
Earlier work this paper cites.
J. Ebrahimi, A. Rao, D. Lowd, and D. Dou, “Hotflip: White-box adversarial examples for text classification,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Association for Computational Linguistics, 2018, pp. 31–36
2018
Cited alongside, same era.
Y. Belinkov and Y. Bisk, “Synthetic and natural noise both break neural machine translation,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
J. Ebrahimi, D. Lowd, and D. Dou, “On adversarial examples for character-level neural machine translation,” in Proceedings of the 27th International Conference on Computational Linguistics . Association for Computational Linguistics, 2018, pp. 653–663
2018
Cited alongside, same era.
Y. Cheng, Z. Tu, F. Meng, J. Zhai, and Y. Liu, “Towards robust neural machine translation,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 1756–1766
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings , 2018
2018
Later among the works it cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186
2019
Closest in time.
Y. Cheng, L. Jiang, and W. Macherey, “Robust neural machine translation with doubly adversarial inputs,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 4324–4333
2019
Closest in time.
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2018
Cited alongside, same era.
Y. Qi, D. Sachan, M. Felix, S. Padmanabhan, and G. Neubig, “When and why are pre-trained word embeddings useful for neural machine translation?” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) . New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 529–535
2018
Cited alongside, same era.
D. Sachan and G. Neubig, “Parameter sharing methods for multilingual self-attentional translation models,” in Proceedings of the Third Conference on Machine Translation . Association for Computational Linguistics, 2018
2018
Cited alongside, same era.
T. He and J. Glass, “Detecting egregious responses in neural sequence-to-sequence models,” in International Conference on Learning Representations , 2019
2019
Closest in time.
H. Liu, M. Ma, L. Huang, H. Xiong, and Z. He, “Robust neural machine translation with joint textual and phonetic embedding,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 3044–3049
2019
Closest in time.