Multi-Stage Training for Abusive Comment Detection in Indic Languages

2026-05-21 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new pipeline for abusive comment detection in Indic languages has been proposed, focusing on minimizing false-positive rates to safeguard freedom of expression on social media. Published on 2026-05-21, this system integrates language-based preprocessing with an ensemble of multiple models. The research addresses the critical need for safe online communication spaces, given the widespread use of social media for sharing ideas and information. Through extensive experimentation, the authors analyzed the performance of various models, ultimately developing a robust detection system designed to accurately identify abusive content without incorrectly flagging non-abusive comments, thereby ensuring social media remains a secure platform for diverse discussions.

Key takeaway

For NLP Engineers developing content moderation systems for Indic languages, this research highlights the importance of a multi-stage approach. You should integrate language-based preprocessing and ensemble modeling to achieve high detection accuracy while critically minimizing false positives. Prioritizing false-positive reduction ensures your system protects freedom of expression, a key consideration for social media platforms. This approach can enhance the reliability and fairness of your abusive comment detection tools.

Key insights

A multi-stage pipeline combining language preprocessing and model ensembles effectively detects abusive Indic language comments while minimizing false positives.

Principles

• Minimize false positives to protect free expression.
• Language-specific preprocessing is crucial.
• Ensemble models enhance detection accuracy.

Method

The proposed pipeline uses language-based preprocessing followed by an ensemble of several models. Extensive experimentation analyzes performance to minimize false-positive rates in abusive comment detection.

In practice

• Implement language-specific preprocessing.
• Combine multiple detection models.
• Prioritize false-positive rate reduction.

Topics

• Abusive Comment Detection
• Indic Languages
• Multi-Stage Training
• Ensemble Models
• Content Moderation
• False Positive Rate

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.