UPR at SemEval-2026 Task 9: Polarization Detection in Urdu with Language-Specific Transformer and Data Augmentation
Year: 2026
Type: conference-paper
Abstract: This paper addresses polarization detection in Urdu, a low-resource language characterized by complex morphology and insufficient annotated data.We formulate the task as a binary classification problem of social media posts into polarized and non-polarized categories.Our approach is based on Urdu-BERT, a language-specific transformer model combined with language-specific preprocessing, duplicate removal, and data augmentation to mitigate class imbalance and improve generalization.Experimental results show that the fine-tuned Urdu-BERT outperforms TF-IDFbased lexical machine learning baselines and achieves strong performance relative to multilingual transformer baselines.The findings indicate that language-specific pretrained transformers, when combined with appropriate preprocessing and augmentation strategies, provide an effective and generalizable framework for low-resource Urdu polarization detection.
Institutions: University of Poonch Rawalakot, Sejong University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166836609