@inproceedings{s-etal-2026-polarmind,
title = "{P}olar{M}ind at {S}em{E}val-2026 Task 9: Leveraging {L}a{BSE} with Progressive Curriculum Learning for Multicultural Polarization",
author = "s, Sandeep and
M, Mothish and
Sundar, Sachin",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.405/",
pages = "3232--3239",
ISBN = "979-8-89176-414-9",
abstract = "Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural on texts 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 activearea 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{---}toobtain strong cross-lingual learning which enhances scores in low-resource language by ascore up to 0.2 macro F1. Furthermore, we provide a comprehensive ablation study evaluatingthe performance of diverse encoder models in the Qwen model family within a retrieval-basedprompting framework."
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<abstract>Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural on texts 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 activearea 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—toobtain strong cross-lingual learning which enhances scores in low-resource language by ascore up to 0.2 macro F1. Furthermore, we provide a comprehensive ablation study evaluatingthe performance of diverse encoder models in the Qwen model family within a retrieval-basedprompting framework.</abstract>
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%0 Conference Proceedings
%T PolarMind at SemEval-2026 Task 9: Leveraging LaBSE with Progressive Curriculum Learning for Multicultural Polarization
%A s, Sandeep
%A M, Mothish
%A Sundar, Sachin
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F s-etal-2026-polarmind
%X Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural on texts 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 activearea 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—toobtain strong cross-lingual learning which enhances scores in low-resource language by ascore up to 0.2 macro F1. Furthermore, we provide a comprehensive ablation study evaluatingthe performance of diverse encoder models in the Qwen model family within a retrieval-basedprompting framework.
%U https://aclanthology.org/2026.semeval-1.405/
%P 3232-3239
Markdown (Informal)
[PolarMind at SemEval-2026 Task 9: Leveraging LaBSE with Progressive Curriculum Learning for Multicultural Polarization](https://aclanthology.org/2026.semeval-1.405/) (s et al., SemEval 2026)
ACL