Robustness to Programmable String Transformations via Augmented Abstract Training

ICML 2020 Yuhao ZhangAws AlbarghouthiLoris D'Antoni

Deep neural networks for natural language processing tasks are vulnerable to adversarial input perturbations. In this paper, we present a versatile language for programmatically specifying string transformations -- e.g., insertions, deletions, substitutions, swaps, etc... (read more)

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