@inproceedings{nestor-etal-2026-polarizedteam,
title = "{P}olarized{T}eam at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization",
author = "Nestor, Maria and
Al Shrafat, Maroan and
Pește, Ioana and
Gifu, Daniela and
Trandab{\u{a}}ț, Diana",
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-1.180/",
pages = "1391--1397",
ISBN = "979-8-89176-414-9",
abstract = "This paper presents the systems developed for SemEval-2026 Task 9, which targets the detection and categorization of multilingual, multicultural, and multi-event online polarization across 22 languages. To address the challenges posed by linguistic diversity and short, heterogeneous texts, we evaluate several Transformer-based architectures for multilingual polarization detection. Our approach models the task as a multi-label classification problem and incorporates mean pooling for sentence representation, focal loss to mitigate severe label imbalance, and label-wise attention mechanisms to capture polarization-specific linguistic cues. Experimental results show that combining robust multilingual encoders with label-aware modelling substantially improves the detection of polarized content across diverse communities and events"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nestor-etal-2026-polarizedteam">
<titleInfo>
<title>PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Nestor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maroan</namePart>
<namePart type="family">Al Shrafat</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ioana</namePart>
<namePart type="family">Pește</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniela</namePart>
<namePart type="family">Gifu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Diana</namePart>
<namePart type="family">Trandabăț</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Kochmar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Debanjan</namePart>
<namePart type="family">Ghosh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kai</namePart>
<namePart type="family">North</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mamoru</namePart>
<namePart type="family">Komachi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-414-9</identifier>
</relatedItem>
<abstract>This paper presents the systems developed for SemEval-2026 Task 9, which targets the detection and categorization of multilingual, multicultural, and multi-event online polarization across 22 languages. To address the challenges posed by linguistic diversity and short, heterogeneous texts, we evaluate several Transformer-based architectures for multilingual polarization detection. Our approach models the task as a multi-label classification problem and incorporates mean pooling for sentence representation, focal loss to mitigate severe label imbalance, and label-wise attention mechanisms to capture polarization-specific linguistic cues. Experimental results show that combining robust multilingual encoders with label-aware modelling substantially improves the detection of polarized content across diverse communities and events</abstract>
<identifier type="citekey">nestor-etal-2026-polarizedteam</identifier>
<location>
<url>https://aclanthology.org/2026.semeval-1.180/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>1391</start>
<end>1397</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization
%A Nestor, Maria
%A Al Shrafat, Maroan
%A Pește, Ioana
%A Gifu, Daniela
%A Trandabăț, Diana
%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 nestor-etal-2026-polarizedteam
%X This paper presents the systems developed for SemEval-2026 Task 9, which targets the detection and categorization of multilingual, multicultural, and multi-event online polarization across 22 languages. To address the challenges posed by linguistic diversity and short, heterogeneous texts, we evaluate several Transformer-based architectures for multilingual polarization detection. Our approach models the task as a multi-label classification problem and incorporates mean pooling for sentence representation, focal loss to mitigate severe label imbalance, and label-wise attention mechanisms to capture polarization-specific linguistic cues. Experimental results show that combining robust multilingual encoders with label-aware modelling substantially improves the detection of polarized content across diverse communities and events
%U https://aclanthology.org/2026.semeval-1.180/
%P 1391-1397
Markdown (Informal)
[PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization](https://aclanthology.org/2026.semeval-1.180/) (Nestor et al., SemEval 2026)
ACL