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arXiv · 2606.21718

Leveraging LaBSE with Progressive Curriculum Learning for Multicultural Polarization

Sachin Sundar·Sandeep Kumar·Mothish M
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Abstract

Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural contexts where intergroup hostility is prevalent. The problem is particularly challenging due to the data scarcity for these tasks in the low-resource languages. Identifying such phenomena has become an active area of research and is addressed in SemEval-2026 Task 9: Multilingual, Multicultural Online Polarization Detection. To address this problem we propose an architecture that leverages LaBSE embeddings - an unconventional choice typically reserved for retrieval tasks, to obtain strong cross-lingual learning which enhances scores in low-resource language by a score up to 0.2 macro F1. Furthermore, we provide a comprehensive ablation study evaluating the performance of diverse encoder models in the Qwen model family within a retrieval-based prompting framework. Our code will be soon available at https://github.com/carrycurious/PolarMind.

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Sachin Sundar, Sandeep Kumar, Mothish M. 2026-06-19. Leveraging LaBSE with Progressive Curriculum Learning for Multicultural Polarization. https://arxiv.org/abs/2606.21718

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