Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment

27 Jul 2019Di JinZhijing JinJoey Tianyi ZhouPeter Szolovits

Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples... (read more)

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