@inproceedings{fetouh-etal-2026-reglat-semeval,
title = "{REGLAT} at {S}em{E}val-2026 Task 9: Enhancing {A}rabic Online Polarization Detection Using {A}ra{BERT} and Synonym Replacement Augmentation",
author = "Fetouh, Ahmed and
Francies, Mariam and
Ashraf, Nsrin and
Nayel, Hamada and
Mohammed, Rahmath",
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-1.226/",
pages = "1779--1783",
ISBN = "979-8-89176-414-9",
abstract = "In this paper, we present our system, which was submitted to SemEval-2026 Task 9 (Subtask 1: Polarization Detection) and focuses on binary classification of polarized content in Arabic social media text. To address Arabic linguistic variations, we propose a single-model approach that combines fine-tuned AraBERT with synonym-based data augmentation. On the Arabic bind set, our method achieves a competitive macro F1-score of 0.831 and an accuracy of 0.833. Among the 45 participating teams, our system ranked 11th overall, with a performance gap of 0.018 macro F1 from the top-ranked team (0.8488). The results show that a fine-tuned AraBERT with synonym replacement is a strong, simple, and reproducible baseline that outperforms more complex setups in dealing with Arabic attitude polarization nuances."
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<abstract>In this paper, we present our system, which was submitted to SemEval-2026 Task 9 (Subtask 1: Polarization Detection) and focuses on binary classification of polarized content in Arabic social media text. To address Arabic linguistic variations, we propose a single-model approach that combines fine-tuned AraBERT with synonym-based data augmentation. On the Arabic bind set, our method achieves a competitive macro F1-score of 0.831 and an accuracy of 0.833. Among the 45 participating teams, our system ranked 11th overall, with a performance gap of 0.018 macro F1 from the top-ranked team (0.8488). The results show that a fine-tuned AraBERT with synonym replacement is a strong, simple, and reproducible baseline that outperforms more complex setups in dealing with Arabic attitude polarization nuances.</abstract>
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%0 Conference Proceedings
%T REGLAT at SemEval-2026 Task 9: Enhancing Arabic Online Polarization Detection Using AraBERT and Synonym Replacement Augmentation
%A Fetouh, Ahmed
%A Francies, Mariam
%A Ashraf, Nsrin
%A Nayel, Hamada
%A Mohammed, Rahmath
%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 fetouh-etal-2026-reglat-semeval
%X In this paper, we present our system, which was submitted to SemEval-2026 Task 9 (Subtask 1: Polarization Detection) and focuses on binary classification of polarized content in Arabic social media text. To address Arabic linguistic variations, we propose a single-model approach that combines fine-tuned AraBERT with synonym-based data augmentation. On the Arabic bind set, our method achieves a competitive macro F1-score of 0.831 and an accuracy of 0.833. Among the 45 participating teams, our system ranked 11th overall, with a performance gap of 0.018 macro F1 from the top-ranked team (0.8488). The results show that a fine-tuned AraBERT with synonym replacement is a strong, simple, and reproducible baseline that outperforms more complex setups in dealing with Arabic attitude polarization nuances.
%U https://aclanthology.org/2026.semeval-1.226/
%P 1779-1783
Markdown (Informal)
[REGLAT at SemEval-2026 Task 9: Enhancing Arabic Online Polarization Detection Using AraBERT and Synonym Replacement Augmentation](https://aclanthology.org/2026.semeval-1.226/) (Fetouh et al., SemEval 2026)
ACL