Transformer-based models have pushed state of the art in many areas of NLP, but our
understanding of what is behind their success is still limited. This paper is the first survey of …

As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by
inaccessible historical data; accordingly, if the data (information) were memorized perfectly …

Vector-based word representation paradigms situate lexical meaning at different levels of
abstraction. Distributional and static embedding models generate a single vector per word …

Intent detection and slot filling are the two most essential tasks of natural language
understanding (NLU). Deep neural models have produced impressive results on these …

Sentence pair modelling is defined as the task of identifying the semantic interaction
between a sentence pair, ie., paraphrase and textual entailment identification and semantic …

This paper applies Natural Language Processing (NLP) methods to analyze the exposure to
trauma experienced by witnesses in international criminal tribunals when testifying in court …

Transformers are responsible for the vast majority of recent advances in natural language
processing. The majority of practical natural language processing applications of these …

As the insulting statements increase on the online platform, these negative statements
create a reaction and disturb the peace of society. Offensive language detection research …

Enhancing learning effectiveness requires one to define suitable learning outcomes and
align assessment constructs with these outcomes. We present a quad-faceted feature-based …

Phishing, a prevalent cybercrime tactic for decades, remains a significant threat in today's
digital world. By leveraging clever social engineering elements and modern technology …