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Large-scale pre-trained language models have shown outstanding performance in a variety of NLP tasks.
Roberta: A robustly optimized bert pretraining approach
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Generating natural language adversarial examples
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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HotFlip: White-box adversarial examples for text classification
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Towards robust neural networks via random self-ensemble
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Certified adversarial robustness via randomized smoothing
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Towards robustness against natural language word substitutions
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Searching for an effective defender: Benchmarking defense against adversarial word substitution
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Masker: Masked keyword regularization for reliable text classification
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Certified robustness to word substitution attack with differential privacy
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Towards improving adversarial training of NLP models
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Defense against synonym substitution-based adversarial attacks via Dirichlet neighborhood ensemble
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Exploring the limits of transfer learning with a unified text-to-text transformer
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