Combating Hate: How Multilingual Transformers Can Help Detect Topical Hate Speech

13 pages•Published: May 26, 2023

Abstract

Automated hate speech detection is important to protecting people’s dignity, online experiences, and physical safety in Society 5.0. Transformers are sophisticated pre- trained language models that can be fine-tuned for multilingual hate speech detection. Many studies consider this application as a binary classification problem. Additionally, research on topical hate speech detection use target-specific datasets containing assertions about a particular group. In this paper we investigate multi-class hate speech detection using target-generic datasets. We assess the performance of mBERT and XLM-RoBERTA on high and low resource languages, with limited sample sizes and class imbalance. We find that our fine-tuned mBERT models are performant in detecting gender-targeted hate speech. Our Urdu classifier produces a 31% lift on the baseline model. We also present a pipeline for processing multilingual datasets for multi-class hate speech detection. Our approach could be used in future works on topically focused hate speech detection for other low resource languages, particularly African languages which remain under-explored in this domain.

Keyphrases: hate speech, machine learning, Natural Language Processing

In: Aurona Gerber and Knut Hinkelmann (editors). Proceedings of Society 5.0 Conference 2023, vol 93, pages 203--215

Links:	https://easychair.org/publications/paper/28NM
	https://doi.org/10.29007/1cm6