# Multi-task Learning with Active Learning for Arabic Offensive Speech Detection

arXiv: https://arxiv.org/abs/2506.02753 · HTML: https://mlsanity.com/paper/2506.02753/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 71 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study](https://mlsanity.com/paper/1808.05697/) (2018)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data](https://mlsanity.com/paper/2009.09139/) (2020)
- [Emotion Based Hate Speech Detection using Multimodal Learning](https://mlsanity.com/paper/2202.06218/) (2022)
- [Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech](https://mlsanity.com/paper/2201.06723/) (2022)
- [OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach](https://mlsanity.com/paper/2005.07297/) (2020)
- [Leveraging Affective Bidirectional Transformers for Offensive Language Detection](https://mlsanity.com/paper/2006.01266/) (2020)
- [Active learning using adaptable task-based prioritisation](https://mlsanity.com/paper/2212.01703/) (2022)
- [Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media](https://mlsanity.com/paper/2101.11155/) (2021)
- [Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing](https://mlsanity.com/paper/1908.07820/) (2019)
- [SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection](https://mlsanity.com/paper/2007.13974/) (2020)
- [AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify](https://mlsanity.com/paper/2207.08557/) (2022)
- [Explainable and High-Performance Hate and Offensive Speech Detection](https://mlsanity.com/paper/2206.12983/) (2022)
- [MHS-STMA: Multimodal Hate Speech Detection via Scalable Transformer-Based Multilevel Attention Framework](https://mlsanity.com/paper/2409.05136/) (2024)

## Abstract (arXiv)

The rapid growth of social media has amplified the spread of offensive, violent, and vulgar speech, which poses serious societal and cybersecurity concerns. Detecting such content in Arabic text is particularly complex due to limited labeled data, dialectal variations, and the language's inherent complexity. This paper proposes a novel framework that integrates multi-task learning (MTL) with active learning to enhance offensive speech detection in Arabic social media text. By jointly training on two auxiliary tasks, violent and vulgar speech, the model leverages shared representations to improve the detection accuracy of the offensive speech. Our approach dynamically adjusts task weights during training to balance the contribution of each task and optimize performance. To address the scarcity of labeled data, we employ an active learning strategy through several uncertainty sampling techniques to iteratively select the most informative samples for model training. We also introduce weighted emoji handling to better capture semantic cues. Experimental results on the OSACT2022 dataset show that the proposed framework achieves a state-of-the-art macro F1-score of 85.42%, outperforming existing methods while using significantly fewer fine-tuning samples. The findings of this study highlight the potential of integrating MTL with active learning for efficient and accurate offensive language detection in resource-constrained settings.