Work

DigiS-FBK at SemEval-2026 Task 9: Multi-task Learning for Multilingual and Cross-cultural Polarization Classification

Year: 2026

Type: conference-paper

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.1

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.357

Authors: Veronica Orsanigo, Alan Ramponi, Elisa Leonardelli

Institutions: University of Trento, Fondazione Bruno Kessler

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, HORIZON EUROPE Framework Programme

Award: Strengthening Democratic Resilience Through Digital Twins