Multi-Stage Training for Abusive Comment Detection in Indic Languages

2026-05-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A research paper titled "Multi-Stage Training for Abusive Comment Detection in Indic Languages" (arXiv:2605.22380), authored by Pranshu Rastogi, Madhav Mathur, Ramaneswaran S, and Kshitij Mohan, introduces a novel pipeline for identifying abusive comments on social media. Submitted on May 21, 2026, this 4-page work specifically addresses content in Indic languages, where effective moderation is crucial given the widespread use of social platforms. The proposed system integrates language-based preprocessing with an ensemble of several machine learning models. Through extensive experimentation, the authors aim to minimize the false-positive rate, ensuring that non-abusive content is not incorrectly flagged. This approach seeks to balance effective abuse detection with the preservation of freedom of expression in online communication environments.

Key takeaway

For NLP Engineers developing content moderation systems for Indic languages, you should consider implementing a multi-stage training pipeline. This approach, combining language-based preprocessing with an ensemble of models, is shown to minimize false positives, which is critical for upholding freedom of expression. Prioritize rigorous evaluation of false positive rates to ensure your system effectively detects abuse without over-censoring.

Key insights

Multi-stage training with language preprocessing and model ensembles improves abusive comment detection while minimizing false positives.

Principles

• Prioritize minimizing false positives in abuse detection.
• Language-specific preprocessing enhances model performance.
• Ensemble models improve detection accuracy.

Method

The proposed pipeline combines language-based preprocessing with an ensemble of multiple models to detect abusive comments, specifically designed to minimize false positives.

In practice

• Implement language-specific text normalization.
• Combine diverse classifiers for robust detection.
• Evaluate systems primarily on false positive rates.

Topics

• Abusive Content Detection
• Indic Languages
• Multi-Stage Training
• Ensemble Models
• Natural Language Processing
• Content Moderation

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.