Before revision: While there has been great interest in generating imperceptible adversarial examples in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text in the discrete domain is still challenging .
After revision: Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial example generation in continuous data domain (e.g. image and audio)to explore the model vulnerabilities, generatingadversarial text in the discrete domain is still challenging .