@inproceedings{taien-hossen-2026-taien,
title = "Taien at {S}em{E}val-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models",
author = "Taien, Saida and
Hossen, Palash",
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-1.260/",
pages = "2070--2077",
ISBN = "979-8-89176-414-9",
abstract = "This submission describes a multilingual polarization detection system for SemEval-2026 Task 9. The system leverages parallel fine-tuning of XLM-RoBERTa and mDeBERTa-v3 transformer models with a probability-level ensemble to improve prediction reliability. We employ language-independent preprocessing, subword tokenization, and a standardized classification head for all 22 languages to ensure a consistent modeling framework across the multilingual setting. Experimental results demonstrate strong performance on both high-resource and low-resource languages, highlighting the effectiveness of the ensemble approach in stabilizing predictions and improving multilingual polarization detection."
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<abstract>This submission describes a multilingual polarization detection system for SemEval-2026 Task 9. The system leverages parallel fine-tuning of XLM-RoBERTa and mDeBERTa-v3 transformer models with a probability-level ensemble to improve prediction reliability. We employ language-independent preprocessing, subword tokenization, and a standardized classification head for all 22 languages to ensure a consistent modeling framework across the multilingual setting. Experimental results demonstrate strong performance on both high-resource and low-resource languages, highlighting the effectiveness of the ensemble approach in stabilizing predictions and improving multilingual polarization detection.</abstract>
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%0 Conference Proceedings
%T Taien at SemEval-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models
%A Taien, Saida
%A Hossen, Palash
%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 taien-hossen-2026-taien
%X This submission describes a multilingual polarization detection system for SemEval-2026 Task 9. The system leverages parallel fine-tuning of XLM-RoBERTa and mDeBERTa-v3 transformer models with a probability-level ensemble to improve prediction reliability. We employ language-independent preprocessing, subword tokenization, and a standardized classification head for all 22 languages to ensure a consistent modeling framework across the multilingual setting. Experimental results demonstrate strong performance on both high-resource and low-resource languages, highlighting the effectiveness of the ensemble approach in stabilizing predictions and improving multilingual polarization detection.
%U https://aclanthology.org/2026.semeval-1.260/
%P 2070-2077
Markdown (Informal)
[Taien at SemEval-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models](https://aclanthology.org/2026.semeval-1.260/) (Taien & Hossen, SemEval 2026)
ACL