NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models

• URL: http://arxiv.org/abs/2412.08163v2
• Date: Thu, 12 Dec 2024 11:42:11 GMT
• Title: NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models
• Authors: Anmol Guragain, Nadika Poudel, Rajesh Piryani, Bishesh Khanal,
• Abstract summary: This paper focuses on hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali. Using a range of transformer-based models, we examine their effectiveness in navigating the nuanced boundary between hate speech and free expression. This work emphasizes the need for hate speech detection in Devanagari-scripted languages and presents a foundation for further research.
• Score: 0.9974630621313314
• License:
• Abstract: This paper explores hate speech detection in Devanagari-scripted languages, focusing on Hindi and Nepali, for Subtask B of the CHIPSAL@COLING 2025 Shared Task. Using a range of transformer-based models such as XLM-RoBERTa, MURIL, and IndicBERT, we examine their effectiveness in navigating the nuanced boundary between hate speech and free expression. Our best performing model, implemented as ensemble of multilingual BERT models achieve Recall of 0.7762 (Rank 3/31 in terms of recall) and F1 score of 0.6914 (Rank 17/31). To address class imbalance, we used backtranslation for data augmentation, and cosine similarity to preserve label consistency after augmentation. This work emphasizes the need for hate speech detection in Devanagari-scripted languages and presents a foundation for further research.

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