In recent years, due to the widespread use of social media and broadcasting agencies
around the world, people are extremely exposed to being affected by false information and …

The growing use of social media has led to the development of several Machine Learning
(ML) and Natural Language Processing (NLP) tools to process the unprecedented amount …

In this paper, we address the issue of augmenting text data in supervised Natural Language
Processing problems, exemplified by deep online hate speech classification. A great …

The growing use of media has led to the development of several machine learning (ML) and
natural language processing (NLP) tools to process the unprecedented amount of social …

Text classification is a fundamental and important area of natural language processing for
assigning a text into at least one predefined tag or category according to its content. Most of …

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

Very recently, few certified defense methods have been developed to provably guarantee
the robustness of a text classifier to adversarial synonym substitutions. However, all the …

Existing studies have demonstrated that adversarial examples can be directly attributed to
the presence of non-robust features, which are highly predictive, but can be easily …

In this paper we apply self-knowledge distillation to text summarization which we argue can
alleviate problems with maximum-likelihood training on single reference and noisy datasets …

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