TAMILGOODBADTXT@DravidianLangTech 2026:A Multilingual Transformer-Based Approach for Abusive Language Identification in Tamil Social Media

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Varalakshmi K and Bharathi B, in their 2026 paper "TAMILGOODBADTXT@DravidianLangTech 2026:A Multilingual Transformer-Based Approach for Abusive Language Identification in Tamil Social Media," introduce a multilingual transformer-based method for identifying abusive language in Tamil social media. Detecting such content is challenging in low-resource languages like Tamil due to prevalent spelling errors, informal expressions, and code-mixing. Their proposed general pipeline utilizes a pretrained XLM-RoBERTa model to extract contextual and semantic representations from input text, followed by preprocessing, tokenization, and binary classification into abusive or non-abusive categories. Experiments conducted on Tamil social media datasets demonstrated that this approach achieved an F1-score of 78.64%, highlighting the effectiveness of cross-lingual pretrained models for abusive language detection in low-resource linguistic contexts.

Key takeaway

For NLP Engineers developing content moderation systems for low-resource languages, this research suggests adopting multilingual transformer models. Your efforts to detect abusive language in contexts like Tamil social media can benefit significantly from pretrained models such as XLM-RoBERTa, which achieved an F1-score of 78.64%. Consider integrating a preprocessing, tokenization, and binary classification pipeline to address challenges like code-mixing and informal expressions effectively.

Key insights

Multilingual transformers effectively identify abusive language in low-resource social media, overcoming linguistic complexities.

Principles

• Cross-lingual models excel in low-resource language tasks.
• Pretrained transformers capture complex social media text.

Method

A general pipeline involves preprocessing, tokenization, and binary classification using a pretrained XLM-RoBERTa model for contextual learning.

In practice

• Apply XLM-RoBERTa for low-resource NLP tasks.
• Implement binary classification for content moderation.

Topics

• Abusive Language Detection
• Tamil Language Processing
• Multilingual Transformers
• XLM-RoBERTa
• Low-Resource NLP
• Social Media Analysis

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.