Meta AI at Arabic Hate Speech 2022: MultiTask Learning with Self-Correction for Hate Speech Classification
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 48 references that are in the map. First posted on arXiv 16 May 2022.
Builds on
19 of 48 references.
References · 19
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7 more
- 2020ARBERT & MARBERT: Deep Bidirectional Transformers for Arabiccited by 164
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Citing papers per year: 2022: 2.
2 papers.
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- 2026X-MuTeST: A Multilingual Benchmark for Explainable Hate Speech Detection and A Novel LLM-consulted Explanation Framework
- 2025Rethinking Hate Speech Detection on Social Media: Can LLMs Replace Traditional Models?
- 2026Is AI Ready for Multimodal Hate Speech Detection? A Comprehensive Dataset and Benchmark Evaluation ↗
- 2025HatePrototypes: Interpretable and Transferable Representations for Implicit and Explicit Hate Speech Detection
- 2024Towards Efficient and Explainable Hate Speech Detection via Model Distillation
- 2026Sledgehammer or Scalpel? A Fine-grained Adaptive Framework for Implicit Hate Speech
- 2026AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes
- 2023HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models