Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media
This work addresses the challenge of detecting toxic content like misogyny for Arabic social media users, representing an incremental improvement using existing methods on new data.
The paper tackled the problem of identifying and categorizing misogyny on Arabic social media by developing multi-task learning models based on MARBERT, achieving top-three ranked performances in both tasks.
The prevalence of toxic content on social media platforms, such as hate speech, offensive language, and misogyny, presents serious challenges to our interconnected society. These challenging issues have attracted widespread attention in Natural Language Processing (NLP) community. In this paper, we present the submitted systems to the first Arabic Misogyny Identification shared task. We investigate three multi-task learning models as well as their single-task counterparts. In order to encode the input text, our models rely on the pre-trained MARBERT language model. The overall obtained results show that all our submitted models have achieved the best performances (top three ranked submissions) in both misogyny identification and categorization tasks.