Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks

Understand

Real-world natural language processing systems need to be robust to human adversaries.

• Collecting examples of human adversaries for training is an effective but expensive solution.
• On the other hand, training on synthetic attacks with small perturbations - such as word-substitution - does not actually improve robustness to human adversaries.
• In this paper, we propose an adversarial training framework that uses limited human adversarial examples to generate more useful adversarial examples at scale.