UPR at SemEval-2026 Task 9: Polarization Detection in Urdu with Language-Specific Transformer and Data Augmentation

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

Summary

UPR's submission to SemEval-2026 Task 9 addresses polarization detection in Urdu, a low-resource language challenged by complex morphology and insufficient annotated data. The team formulated this as a binary classification problem for social media posts, categorizing them as polarized or non-polarized. Their core approach leverages Urdu-BERT, a language-specific transformer model, integrated with language-specific preprocessing, duplicate removal, and data augmentation techniques. These strategies were implemented to mitigate class imbalance and enhance model generalization. Experimental results demonstrate that the fine-tuned Urdu-BERT significantly outperforms TF-IDF-based lexical machine learning baselines and achieves strong performance compared to multilingual transformer baselines. This indicates that language-specific pretrained transformers, when combined with tailored preprocessing and augmentation, offer an effective framework for low-resource Urdu polarization detection.

Key takeaway

For NLP Engineers developing solutions for low-resource languages like Urdu, you should prioritize language-specific transformer models. This approach, combined with tailored preprocessing and data augmentation, significantly improves performance over multilingual or lexical baselines. Implement duplicate removal and class imbalance mitigation. These steps enhance model generalization for robust polarization detection in challenging linguistic contexts.

Key insights

Language-specific transformers with tailored preprocessing and data augmentation effectively detect polarization in low-resource Urdu.

Principles

• Language-specific transformers excel for low-resource NLP.
• Preprocessing and augmentation are crucial for generalization.
• Class imbalance mitigation improves model performance.

Method

Binary classification of social media posts (polarized/non-polarized) using fine-tuned Urdu-BERT, language-specific preprocessing, duplicate removal, and data augmentation.

In practice

• Apply Urdu-BERT for Urdu text classification tasks.
• Implement data augmentation for low-resource languages.
• Use language-specific preprocessing for morphological complexity.

Topics

• Polarization Detection
• Urdu Language
• Transformer Models
• Data Augmentation
• Low-Resource NLP
• SemEval-2026

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.