Abstract
In this paper, we tackle the Arabic Fine-Grained Hate Speech Detection shared task and demonstrate significant improvements over reported baselines for its three subtasks. The tasks are to predict if a tweet contains (1) Offensive language; and whether it is considered (2) Hate Speech or not and if so, then predict the (3) Fine-Grained Hate Speech label from one of six categories. Our final solution is an ensemble of models that employs multitask learning and a self-consistency correction method yielding 82.7% on the hate speech subtask—reflecting a 3.4% relative improvement compared to previous work.- Anthology ID:
- 2022.osact-1.24
- Volume:
- 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
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Hend Al-Khalifa, Tamer Elsayed, Hamdy Mubarak, Abdulmohsen Al-Thubaity, Walid Magdy, Kareem Darwish
- Venue:
- OSACT
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 186–193
- Language:
- URL:
- https://aclanthology.org/2022.osact-1.24
- DOI:
- Cite (ACL):
- Badr AlKhamissi and Mona Diab. 2022. Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification. In 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, pages 186–193, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification (AlKhamissi & Diab, OSACT 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.osact-1.24.pdf