Pretrained language models (PLMs) perform poorly under adversarial attacks. To improve
the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted …

Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a
real challenge. Firstly, as the sentence space is discrete, it is difficult to make small …

Adversarial robustness has attracted much attention recently, and the mainstream solution is
adversarial training. However, the tradition of generating adversarial perturbations for each …

Large-scale pre-trained language models have achieved tremendous success across a
wide range of natural language understanding (NLU) tasks, even surpassing human …

Despite recent success on various tasks, deep learning techniques still perform poorly on
adversarial examples with small perturbations. While optimization-based methods for …

Pre-trained contextualized language models (PrLMs) have led to strong performance gains
in downstream natural language understanding tasks. However, PrLMs can still be easily …

Recently, the textual adversarial attack models become increasingly popular due to their
successful in estimating the robustness of NLP models. However, existing works have …

Recent studies show that pre-trained language models (LMs) are vulnerable to textual
adversarial attacks. However, existing attack methods either suffer from low attack success …

Adversarial examples expose the vulnerabilities of natural language processing (NLP)
models, and can be used to evaluate and improve their robustness. Existing techniques of …

Despite neural networks have achieved prominent performance on many natural language
processing (NLP) tasks, they are vulnerable to adversarial examples. In this paper, we …