@inproceedings{hossain-2026-axiom-dialectsenteval,
title = "Axiom at {D}ialect{S}ent{E}val 2026: Dialect-Aware Ensembling of {MARBERT}v2 and {A}ra{BERT}v2 for {A}rabic Sentiment Analysis",
author = "Hossain, Md. Ajwad",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.179/",
pages = "1419--1425",
ISBN = "979-8-89176-497-2",
abstract = "Sentiment analysis of regional Arabic dialects presents a formidable challenge due to severe orthographic variation, morphological complexity, and a scarcity of annotated resources. To tackle these linguistic hurdles within Subtask-1 of the DialectSentEval 2026 Shared Task, team Axiom proposes a heavily regularized, dialect-aware ensemble framework combining two domain-complementary language models: MARBERTv2 and AraBERTv2. Our pipeline incorporates preprocessed dialect tags appended directly to input sequences, Stochastic Weight Averaging (SWA) for flat-minima convergence, and simple probability averaging across model outputs. Empirical results on the hidden test set demonstrate the robustness of this approach, with our system securing 2nd place overall and achieving an official Macro F1-Score of 0.8211."
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<abstract>Sentiment analysis of regional Arabic dialects presents a formidable challenge due to severe orthographic variation, morphological complexity, and a scarcity of annotated resources. To tackle these linguistic hurdles within Subtask-1 of the DialectSentEval 2026 Shared Task, team Axiom proposes a heavily regularized, dialect-aware ensemble framework combining two domain-complementary language models: MARBERTv2 and AraBERTv2. Our pipeline incorporates preprocessed dialect tags appended directly to input sequences, Stochastic Weight Averaging (SWA) for flat-minima convergence, and simple probability averaging across model outputs. Empirical results on the hidden test set demonstrate the robustness of this approach, with our system securing 2nd place overall and achieving an official Macro F1-Score of 0.8211.</abstract>
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%0 Conference Proceedings
%T Axiom at DialectSentEval 2026: Dialect-Aware Ensembling of MARBERTv2 and AraBERTv2 for Arabic Sentiment Analysis
%A Hossain, Md. Ajwad
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F hossain-2026-axiom-dialectsenteval
%X Sentiment analysis of regional Arabic dialects presents a formidable challenge due to severe orthographic variation, morphological complexity, and a scarcity of annotated resources. To tackle these linguistic hurdles within Subtask-1 of the DialectSentEval 2026 Shared Task, team Axiom proposes a heavily regularized, dialect-aware ensemble framework combining two domain-complementary language models: MARBERTv2 and AraBERTv2. Our pipeline incorporates preprocessed dialect tags appended directly to input sequences, Stochastic Weight Averaging (SWA) for flat-minima convergence, and simple probability averaging across model outputs. Empirical results on the hidden test set demonstrate the robustness of this approach, with our system securing 2nd place overall and achieving an official Macro F1-Score of 0.8211.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.179/
%P 1419-1425
Markdown (Informal)
[Axiom at DialectSentEval 2026: Dialect-Aware Ensembling of MARBERTv2 and AraBERTv2 for Arabic Sentiment Analysis](https://aclanthology.org/2026.arabicnlp-sharedtasks.179/) (Hossain, ArabicNLP 2026)
ACL