Online toxic discourses could result in conflicts between groups or harm to online
communities. Hate speech is complex and multifaceted harmful or offensive content …

With the multiplication of social media platforms, which offer anonymity, easy access and
online community formation and online debate, the issue of hate speech detection and …

AbstractScale the model, scale the data, scale the compute'is the reigning sentiment in the
world of generative AI today. While the impact of model scaling has been extensively …

We introduce Dynabench, an open-source platform for dynamic dataset creation and model
benchmarking. Dynabench runs in a web browser and supports human-and-model-in-the …

In this paper, we introduce HateBERT, a re-trained BERT model for abusive language
detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit …

The identification of Hate Speech in Social Media is of great importance and receives much
attention in the text classification community. There is a huge demand for research for …

Detecting online hate is a difficult task that even state-of-the-art models struggle with.
Typically, hate speech detection models are evaluated by measuring their performance on …

Hate speech is one type of harmful online content which directly attacks or promotes hate
towards a group or an individual member based on their actual or perceived aspects of …

We present a human-and-model-in-the-loop process for dynamically generating datasets
and training better performing and more robust hate detection models. We provide a new …

This paper presents the HASOC track and its two parts. HASOC is dedicated to evaluate
technology for finding Offensive Language and Hate Speech. HASOC is creating test …