Mohamed Aziz Bennessir


iCompass at Arabic Hate Speech 2022: Detect Hate Speech Using QRNN and Transformers
Mohamed Aziz Bennessir | Malek Rhouma | Hatem Haddad | Chayma Fourati
Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection

This paper provides a detailed overview of the system we submitted as part of the OSACT2022 Shared Tasks on Fine-Grained Hate Speech Detection on Arabic Twitter, its outcome, and limitations. Our submission is accomplished with a hard parameter sharing Multi-Task Model that consisted of a shared layer containing state-of-the-art contextualized text representation models such as MarBERT, AraBERT, ArBERT and task specific layers that were fine-tuned with Quasi-recurrent neural networks (QRNN) for each down-stream subtask. The results show that MARBERT fine-tuned with QRNN outperforms all of the previously mentioned models.