Deep learning models have excelled in solving many problems in Natural Language
Processing, but are susceptible to extensive vulnerabilities. We offer a solution to this …

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

Adversarial training, a method for learning robust deep neural networks, constructs
adversarial examples during training. However, recent methods for generating NLP …

Although deep neural networks have achieved prominent performance on many NLP tasks,
they are vulnerable to adversarial examples. We propose Dirichlet Neighborhood Ensemble …

State-of-the-art deep NLP models have achieved impressive improvements on many tasks.
However, they are found to be vulnerable to some perturbations. Before they are widely …

Building an effective adversarial attacker and elaborating on countermeasures for
adversarial attacks for natural language processing (NLP) have attracted a lot of research in …

With the development of high computational devices, deep neural networks (DNNs), in
recent years, have gained significant popularity in many Artificial Intelligence (AI) …

Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is
much less understood whether, and how, predictions can be manipulated with small …

Deep learning models have achieved great success in solving a variety of natural language
processing (NLP) problems. An ever-growing body of research, however, illustrates the …

Deep learning models have achieved great success in many fields, yet they are vulnerable
to adversarial examples. This paper follows a causal perspective to look into the adversarial …