@inproceedings{guo-chang-2026-yeze,
title = "{YEZE} at {S}em{E}val-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling",
author = "Guo, Fengze and
Chang, Yue",
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-1.235/",
pages = "1860--1873",
ISBN = "979-8-89176-414-9",
abstract = "We present a multilingual system for SemEval-2026 Task 9 on detecting and characterizing online polarization across languages, cultures, and events. Our approach participates in all three subtasks and models each subtask independently using a heterogeneous weighted ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base. For the multi-label settings, we adopt weighted binary cross-entropy to mitigate severe label imbalance. The system is trained exclusively on the provided task data and achieves robust performance across languages."
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%0 Conference Proceedings
%T YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
%A Guo, Fengze
%A Chang, Yue
%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 guo-chang-2026-yeze
%X We present a multilingual system for SemEval-2026 Task 9 on detecting and characterizing online polarization across languages, cultures, and events. Our approach participates in all three subtasks and models each subtask independently using a heterogeneous weighted ensemble of XLM-RoBERTa-large and mDeBERTa-v3-base. For the multi-label settings, we adopt weighted binary cross-entropy to mitigate severe label imbalance. The system is trained exclusively on the provided task data and achieves robust performance across languages.
%U https://aclanthology.org/2026.semeval-1.235/
%P 1860-1873
Markdown (Informal)
[YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling](https://aclanthology.org/2026.semeval-1.235/) (Guo & Chang, SemEval 2026)
ACL