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

Anmol Guragain, Nadika Poudel, Rajesh Piryani, Bishesh Khanal
12/11/2024
cs.CL

No official code found. Best implementation: Anmol2059/NLPineers (3 stars, last commit 2024-12-13).

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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Code Implementations(1)

Anmol2059/NLPineersCommunity100%
31Shell, PythonSep 2, 20241 years ago

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Cite this paper

@article{guragain2024nlpineers,
  title  = {NLPineers@ NLU of Devanagari Script Languages 2025: Hate Speech Detection using Ensembling of BERT-based models},
  author = {Anmol Guragain and Nadika Poudel and Rajesh Piryani and Bishesh Khanal},
  year   = {2024},
  eprint = {2412.08163},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.08163v2}
}

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