  We introduce a grey-box adversarial attack and defence framework for
sentiment classification. We address the issues of differentiability, label
preservation and input reconstruction for adversarial attack and defence in one
unified framework. Our results show that once trained, the attacking model is
capable of generating high-quality adversarial examples substantially faster
(one order of magnitude less in time) than state-of-the-art attacking methods.
These examples also preserve the original sentiment according to human
evaluation. Additionally, our framework produces an improved classifier that is
robust in defending against multiple adversarial attacking methods. Code is
available at: https://github.com/ibm-aur-nlp/adv-def-text-dist.
