Deep Learning-based Text Understanding (DLTU) is the backbone technique behind
various applications, including question answering, machine translation, and text …

We propose an efficient method to generate white-box adversarial examples to trick a
character-level neural classifier. We find that only a few manipulations are needed to greatly …

One of the primary challenges limiting the applicability of deep learning is its susceptibility to
learning spurious correlations rather than the underlying mechanisms of the task of interest …

This paper investigates the robustness of NLP against perturbed word forms. While neural
approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they …

Deep neural networks have achieved remarkable results across many language processing
tasks, however they have been shown to be susceptible to overfitting and highly sensitive to …

We address the problem of enhancing model robustness through regularization.
Specifically, we focus on methods that regularize the model posterior difference between …

Although deep neural networks have achieved state-of-the-art performance in various
machine learning tasks, adversarial examples, constructed by adding small non-random …

The landscape of adversarial attacks against text classifiers continues to grow, with new
attacks developed every year and many of them available in standard toolkits, such as …

This survey provides an overview of the challenges of misspellings in natural language
processing (NLP). While often unintentional, misspellings have become ubiquitous in digital …

Adversarial examples, deliberately crafted using small perturbations to fool deep neural
networks, were first studied in image processing and more recently in NLP. While …