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NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input.
BLEU: a method for automatic evaluation of machine translation
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Freelb: Enhanced adversarial training for language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Thomas Goldstein, and Jingjing Liu. 2020 · 2010
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Explaining and harnessing adversarial examples
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Certified robustness to adversarial word substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel, and Percy Liang. 2019 · 2019
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Facebook fair’s wmt19 news translation task submission
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Adversarial domain adaptation for machine reading comprehension
Huazheng Wang, Zhe Gan, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, and Hongning Wang. 2019 · 2019
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Plug and play language models: a simple approach to controlled text generation
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Pretrained transformers improve out-of-distribution robustness
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Stress test evaluation for natural language inference
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