HNK@DravidianLangTech 2026: Investigating Grapheme-Level Normalization for Abusive Tamil Text Classification

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Science & Research — Mathematics & Computational Sciences, Social Sciences & Behavioral Studies · Depth: Expert, medium

Summary

The paper "HNK@DravidianLangTech 2026: Investigating Grapheme-Level Normalization for Abusive Tamil Text Classification" addresses the critical need for automatic identification of abusive content targeting women in regional languages like Tamil, a growing concern with social media prevalence. The research evaluates the performance of a proposed system for binary text classification using IndicBERT, MuRIL, and Tamil-BERT models. A core contribution is the introduction of grapheme-aware normalization, a technique designed to preserve the structural integrity of Tamil characters at the Unicode level. Experimental results demonstrate that the system, when employing the Tamil-BERT model combined with grapheme-aware normalization, achieved superior performance compared to other evaluated models. This approach ultimately secured the third position in the relevant shared task.

Key takeaway

For NLP Engineers developing content moderation systems for Dravidian languages, particularly Tamil, you should integrate grapheme-aware normalization into your text preprocessing pipeline. This technique, proven effective with Tamil-BERT, significantly improves abusive content detection by preserving Unicode-level character integrity. Prioritize specialized language models like Tamil-BERT over more general alternatives for optimal performance in these critical applications, ensuring safer online spaces.

Key insights

Grapheme-aware normalization significantly enhances abusive Tamil text classification with Tamil-BERT.

Principles

• Unicode-level character integrity is crucial for regional language NLP.
• Specialized BERT models outperform general ones for specific language tasks.
• Pre-processing techniques can significantly boost model performance.

Method

The proposed system performs binary text classification for abusive Tamil content targeting women. It integrates grapheme-aware normalization with pre-trained BERT models (IndicBERT, MuRIL, Tamil-BERT) and evaluates their performance.

In practice

• Implement grapheme-aware normalization for Dravidian language NLP.
• Prioritize Tamil-BERT for abusive content detection in Tamil.
• Evaluate normalization impact on regional language text classification.

Topics

• Abusive Text Classification
• Tamil Language Processing
• Grapheme Normalization
• BERT Models
• Content Moderation
• DravidianLangTech

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.