This work examines the robustness of selfattentive neural networks against adversarial input perturbations.
we investigate the attention and feature extraction mechanisms of state-of-the-art recurrent neural networks and self-attentive architectures for sentiment analysis, entailment and machine translation under adversarial attacks.