@inproceedings{orsanigo-etal-2026-digis,
title = "{D}igi{S}-{FBK} at {S}em{E}val-2026 Task 9: Multi-task Learning for Multilingual and Cross-cultural Polarization Classification",
author = "Orsanigo, Veronica and
Ramponi, Alan and
Leonardelli, Elisa",
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-1.357/",
pages = "2838--2851",
ISBN = "979-8-89176-414-9",
abstract = "Online polarization promotes social fragmentation, misinformation, hate, and toxic language. Polarization has been studied from social and communication perspectives, but it can also be addressed computationally as a text classification task. Due to the variety of polarization targets and manifestations, polarization is a complex phenomenon to study, and both detecting and characterizing it are challenging tasks.In this paper, we present the systems submitted by the DigiS-FBK team to SemEval-2026 Task 9 POLAR aimed at detecting polarization in textual content (subtask 1) and identifying its type (subtask 2) and manifestation (subtask 3) in a multilingual, multicultural, and multievent context. Considering the strong link between subtasks, we propose an approach that leverages a multi-task learning paradigm. Our results reveal that, despite the variability in scores across languages, the overall performance when using multi-task learning is higher than when adopting a single task approach in all subtasks"
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<abstract>Online polarization promotes social fragmentation, misinformation, hate, and toxic language. Polarization has been studied from social and communication perspectives, but it can also be addressed computationally as a text classification task. Due to the variety of polarization targets and manifestations, polarization is a complex phenomenon to study, and both detecting and characterizing it are challenging tasks.In this paper, we present the systems submitted by the DigiS-FBK team to SemEval-2026 Task 9 POLAR aimed at detecting polarization in textual content (subtask 1) and identifying its type (subtask 2) and manifestation (subtask 3) in a multilingual, multicultural, and multievent context. Considering the strong link between subtasks, we propose an approach that leverages a multi-task learning paradigm. Our results reveal that, despite the variability in scores across languages, the overall performance when using multi-task learning is higher than when adopting a single task approach in all subtasks</abstract>
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%0 Conference Proceedings
%T DigiS-FBK at SemEval-2026 Task 9: Multi-task Learning for Multilingual and Cross-cultural Polarization Classification
%A Orsanigo, Veronica
%A Ramponi, Alan
%A Leonardelli, Elisa
%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 orsanigo-etal-2026-digis
%X Online polarization promotes social fragmentation, misinformation, hate, and toxic language. Polarization has been studied from social and communication perspectives, but it can also be addressed computationally as a text classification task. Due to the variety of polarization targets and manifestations, polarization is a complex phenomenon to study, and both detecting and characterizing it are challenging tasks.In this paper, we present the systems submitted by the DigiS-FBK team to SemEval-2026 Task 9 POLAR aimed at detecting polarization in textual content (subtask 1) and identifying its type (subtask 2) and manifestation (subtask 3) in a multilingual, multicultural, and multievent context. Considering the strong link between subtasks, we propose an approach that leverages a multi-task learning paradigm. Our results reveal that, despite the variability in scores across languages, the overall performance when using multi-task learning is higher than when adopting a single task approach in all subtasks
%U https://aclanthology.org/2026.semeval-1.357/
%P 2838-2851
Markdown (Informal)
[DigiS-FBK at SemEval-2026 Task 9: Multi-task Learning for Multilingual and Cross-cultural Polarization Classification](https://aclanthology.org/2026.semeval-1.357/) (Orsanigo et al., SemEval 2026)
ACL