@inproceedings{iannielli-etal-2026-minds,
title = "{MINDS} at {S}em{E}val-2026 Task 9: A Multi-Paradigm Approach to Cross-Lingual Polarization Detection",
author = "Iannielli, Angelo and
Maroli, Samuele and
Roberto, Marco and
Sammartino, Stefano and
Vacirca, Valentino and
Savelli, Claudio and
Coppola, Riccardo and
Giobergia, Flavio",
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-1.313/",
pages = "2475--2486",
ISBN = "979-8-89176-414-9",
abstract = "Online polarization has become a central challenge in digital discourse, characterized by hostility, identity-based division, and culturally dependent expressions that vary across languages. Automatically detecting such phenomena is particularly difficult in multilingual settings, where semantic nuance and implicit rhetoric complicate cross-lingual generalization.In this context, we participate in POLAR, a shared task at SemEval 2026 on multilingual polarization detection and categorization across 22 languages. We compare three modeling paradigms: multilingual encoder fine-tuning, translation-based transfer learning, and prompting-based generative reasoning. For the multi-label categorization task, we introduce a two-stage cascaded architecture to mitigate false positives under severe class imbalance.Our results show that multilingual encoders achieve the most robust performance for binary detection, whereas reasoning-based prompting is competitive for fine-grained category classification. This comparative study highlights the strengths and limitations of each paradigm for cross-lingual polarization analysis."
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<abstract>Online polarization has become a central challenge in digital discourse, characterized by hostility, identity-based division, and culturally dependent expressions that vary across languages. Automatically detecting such phenomena is particularly difficult in multilingual settings, where semantic nuance and implicit rhetoric complicate cross-lingual generalization.In this context, we participate in POLAR, a shared task at SemEval 2026 on multilingual polarization detection and categorization across 22 languages. We compare three modeling paradigms: multilingual encoder fine-tuning, translation-based transfer learning, and prompting-based generative reasoning. For the multi-label categorization task, we introduce a two-stage cascaded architecture to mitigate false positives under severe class imbalance.Our results show that multilingual encoders achieve the most robust performance for binary detection, whereas reasoning-based prompting is competitive for fine-grained category classification. This comparative study highlights the strengths and limitations of each paradigm for cross-lingual polarization analysis.</abstract>
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%0 Conference Proceedings
%T MINDS at SemEval-2026 Task 9: A Multi-Paradigm Approach to Cross-Lingual Polarization Detection
%A Iannielli, Angelo
%A Maroli, Samuele
%A Roberto, Marco
%A Sammartino, Stefano
%A Vacirca, Valentino
%A Savelli, Claudio
%A Coppola, Riccardo
%A Giobergia, Flavio
%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 iannielli-etal-2026-minds
%X Online polarization has become a central challenge in digital discourse, characterized by hostility, identity-based division, and culturally dependent expressions that vary across languages. Automatically detecting such phenomena is particularly difficult in multilingual settings, where semantic nuance and implicit rhetoric complicate cross-lingual generalization.In this context, we participate in POLAR, a shared task at SemEval 2026 on multilingual polarization detection and categorization across 22 languages. We compare three modeling paradigms: multilingual encoder fine-tuning, translation-based transfer learning, and prompting-based generative reasoning. For the multi-label categorization task, we introduce a two-stage cascaded architecture to mitigate false positives under severe class imbalance.Our results show that multilingual encoders achieve the most robust performance for binary detection, whereas reasoning-based prompting is competitive for fine-grained category classification. This comparative study highlights the strengths and limitations of each paradigm for cross-lingual polarization analysis.
%U https://aclanthology.org/2026.semeval-1.313/
%P 2475-2486
Markdown (Informal)
[MINDS at SemEval-2026 Task 9: A Multi-Paradigm Approach to Cross-Lingual Polarization Detection](https://aclanthology.org/2026.semeval-1.313/) (Iannielli et al., SemEval 2026)
ACL
- Angelo Iannielli, Samuele Maroli, Marco Roberto, Stefano Sammartino, Valentino Vacirca, Claudio Savelli, Riccardo Coppola, and Flavio Giobergia. 2026. MINDS at SemEval-2026 Task 9: A Multi-Paradigm Approach to Cross-Lingual Polarization Detection. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2475–2486, San Diego, California, USA. Association for Computational Linguistics.