Bidirectional Encoder Representations from Transformers (BERT) has gained increasing
attention from researchers and practitioners as it has proven to be an invaluable technique …

The rapid progress in deep learning has propelled transformer-based models to the
forefront, establishing them as leading solutions for a multiple NLP tasks. These tasks span …

Toxic content is a global problem, but most resources for detecting toxic content are in
English. When datasets are created in other languages, they often focus exclusively on one …

Keyword extraction is the task of retrieving words that are essential to the content of a given
document. Researchers proposed various approaches to tackle this problem. At the top …

This research investigates changes in online behavior of users who publish in multiple
communities on Reddit by measuring their toxicity at two levels. With the aid of …

Detecting transphobia, homophobia, and various other forms of hate speech is difficult.
Signals can vary depending on factors such as language, culture, geographical region, and …

This study examines the characteristics and behaviours of accounts that propagate hate
speech through their responses to articles posted on five leading digital news media in …

Zero-shot cross-lingual transfer learning has been shown to be highly challenging for tasks
involving a lot of linguistic specificities or when a cultural gap is present between languages …

The growing prevalence and rapid evolution of offensive language in social media amplify
the complexities of detection, particularly highlighting the challenges in identifying such …

Online hate speech is a dangerous phenomenon that can (and should) be promptly
counteracted properly. While Natural Language Processing supplies appropriate algorithms …