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We introduce a grey-box adversarial attack and defence framework forsentiment classification. We address the issues of differentiability, labelpreservation and input reconstruction for adversarial attack and defence in oneunified framework. Our results show that once trained, the attacking model iscapable 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 humanevaluation. Additionally, our framework produces an improved classifier that isrobust in defending against multiple adversarial attacking methods. Code isavailable at: https://github.com/ibm-aur-nlp/adv-def-text-dist.

Grey-box Adversarial Attack And Defence For Sentiment Classification