Scholarlink

The increased use of social media, hate speech poses significant challenges, especially in multilingual regions. This paper evaluates contemporary deep learning models—MuRIL, BERT, RoBERTa, IndicBERT, and XLM-RoBERTa—for Hindi hate speech classification using a dataset of 8,192 multicategorical posts. MuRIL and XLM-RoBERTa achieved F1-scores of 0.955 and 0.945, excelling in code-mixed Hindi-English contexts. The study underscores the need for larger, diverse datasets to enhance model generalization and discusses emerging trends in multilingual hate speech detection.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Hindi Hate Speech Detection: Multilingual & Multimodal Deep Learning Solutions

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Rachna Narula,
  • Udit Hasija,
  • Vijay Kumar,
  • Nitasha Rathore

摘要

The increased use of social media, hate speech poses significant challenges, especially in multilingual regions. This paper evaluates contemporary deep learning models—MuRIL, BERT, RoBERTa, IndicBERT, and XLM-RoBERTa—for Hindi hate speech classification using a dataset of 8,192 multicategorical posts. MuRIL and XLM-RoBERTa achieved F1-scores of 0.955 and 0.945, excelling in code-mixed Hindi-English contexts. The study underscores the need for larger, diverse datasets to enhance model generalization and discusses emerging trends in multilingual hate speech detection.