NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches. We further use our generated adversarial examples to improve models through adversarial training, and we demonstrate that our generated attacks are more robust against model re-training and different model architectures.

本文提出一种名为CAT-Gen的NLP模型，通过可控属性控制生成对抗文本，并将生成的对抗例子用于提高模型的鲁棒性。实验表明，与已有的对抗文本生成方法相比，CAT-Gen能够生成更多样化、更流畅的对抗文本，并且生成的攻击能够更好地抵御模型重新训练和不同的模型结构。

CAT-Gen：通过可控对抗文本生成提升NLP模型的鲁棒性