Year: 2026
Type: conference-paper
Abstract: Detecting abusive language in Tamil social media is a genuinely difficult problem.The language is morphologically rich, speakers routinely mix Tamil with English, and informal romanised Tamil is common enough to confuse models trained primarily on formal text.This work presents a system for binary classification of Tamil comments into Abusive and Non-Abusive categories, submitted to the Dra-vidianLangTech@ACL 2026 shared task [12].MuRIL [5], a BERT-based encoder pre-trained on 17 Indian languages and their transliterated equivalents, is fine-tuned, and it is shown that this Indian-language-specific pre-training provides a meaningful advantage over generic multilingual baselines.The system achieves a macro-averaged F1 of 0.83 on the validation set, compared to 0.79 for XLM-RoBERTa and 0.77 for mBERT under identical training conditions, establishing a strong transformer-based baseline for abusive language detection in codemixed Tamil.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166836937