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New system detects offensive Nastaliq Urdu content with 87% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an explainable system for detecting offensive content in Nastaliq Urdu, a script primarily used on platforms like Facebook. The study involved curating a new corpus of over 7,600 comments, fine-tuning four Urdu-capable transformer models, and comparing their performance against baseline classifiers. The best-performing model achieved 87.05% accuracy, outperforming baselines by a significant margin. Further analysis using word-level SHAP attributions revealed that while performance partly relies on identifying slurs, it also incorporates function words and political vocabulary, suggesting a complex understanding of offensiveness. AI

IMPACT Improves AI's ability to moderate content in under-resourced languages, potentially enabling broader global platform safety.

RANK_REASON Academic paper detailing a new corpus, model benchmarking, and explainability techniques for a specific language and script. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New system detects offensive Nastaliq Urdu content with 87% accuracy

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15 / 100
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Academic paper detailing a new corpus, model benchmarking, and explainability techniques for a specific language and script. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Muhammad Shahid Iqbal Malik ·

    An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution

    arXiv:2610.15795v1 Announce Type: cross Abstract: Automated moderation systems are deployed at scale, yet they remain opaque to the users and moderators affected by their decisions, and their coverage is almost entirely confined to English. For Urdu written in the Nastaliq script…

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