Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

• URL: http://arxiv.org/abs/2303.02513v1
• Date: Sat, 4 Mar 2023 22:28:29 GMT
• Title: Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection
• Authors: Md Rabiul Awal, Roy Ka-Wei Lee, Eshaan Tanwar, Tanmay Garg, Tanmoy Chakraborty
• Abstract summary: Hate speech in social media is a growing phenomenon, and detecting such toxic content has gained significant traction. HateMAML is a model-agnostic meta-learning-based framework that effectively performs hate speech detection in low-resource languages. Extensive experiments are conducted on five datasets across eight different low-resource languages.
• Score: 23.97444551607624
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we propose HateMAML, a model-agnostic meta-learning-based framework that effectively performs hate speech detection in low-resource languages. HateMAML utilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show that HateMAML outperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics of HateMAML.

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