@inproceedings{hossain-2026-axiom-stanceeval,
title = "Axiom at {S}tance{E}val-2026: A Two-Stage Hybrid Cascade for {A}rabic Stance Detection",
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.68/",
pages = "524--528",
ISBN = "979-8-89176-497-2",
abstract = "Cross-target Arabic stance detection presents a unique challenge due to the rich morphological complexity, regional dialects, and heavy reliance on sarcasm within social media discourse. In this paper, we describe the system submitted by Team Axiom for Track 1 (Seen Targets) of the StanceEval-2026 shared task. Our primary contribution is a resource-efficient, Two-Stage Hybrid Cascade architecture. This system utilizes a fine-tuned MARBERTv2 encoder as a high-confidence gatekeeper, dynamically routing uncertain and complex tweets to a locally hosted, 4-bit quantized Large Language Model (Llama-3.1-8B-Instruct). To prevent label bias and ground the LLM{'}s reasoning, the fallback stage incorporates a dynamically balanced, class-specific TF-IDF sub-word retriever. Our official submission achieved an $F_{avg2}$ score of 0.6910 (Rank 18), demonstrating a measurable improvement over our standalone encoder baseline (0.6722)."
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<abstract>Cross-target Arabic stance detection presents a unique challenge due to the rich morphological complexity, regional dialects, and heavy reliance on sarcasm within social media discourse. In this paper, we describe the system submitted by Team Axiom for Track 1 (Seen Targets) of the StanceEval-2026 shared task. Our primary contribution is a resource-efficient, Two-Stage Hybrid Cascade architecture. This system utilizes a fine-tuned MARBERTv2 encoder as a high-confidence gatekeeper, dynamically routing uncertain and complex tweets to a locally hosted, 4-bit quantized Large Language Model (Llama-3.1-8B-Instruct). To prevent label bias and ground the LLM’s reasoning, the fallback stage incorporates a dynamically balanced, class-specific TF-IDF sub-word retriever. Our official submission achieved an F_avg2 score of 0.6910 (Rank 18), demonstrating a measurable improvement over our standalone encoder baseline (0.6722).</abstract>
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%0 Conference Proceedings
%T Axiom at StanceEval-2026: A Two-Stage Hybrid Cascade for Arabic Stance Detection
%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-stanceeval
%X Cross-target Arabic stance detection presents a unique challenge due to the rich morphological complexity, regional dialects, and heavy reliance on sarcasm within social media discourse. In this paper, we describe the system submitted by Team Axiom for Track 1 (Seen Targets) of the StanceEval-2026 shared task. Our primary contribution is a resource-efficient, Two-Stage Hybrid Cascade architecture. This system utilizes a fine-tuned MARBERTv2 encoder as a high-confidence gatekeeper, dynamically routing uncertain and complex tweets to a locally hosted, 4-bit quantized Large Language Model (Llama-3.1-8B-Instruct). To prevent label bias and ground the LLM’s reasoning, the fallback stage incorporates a dynamically balanced, class-specific TF-IDF sub-word retriever. Our official submission achieved an F_avg2 score of 0.6910 (Rank 18), demonstrating a measurable improvement over our standalone encoder baseline (0.6722).
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.68/
%P 524-528
Markdown (Informal)
[Axiom at StanceEval-2026: A Two-Stage Hybrid Cascade for Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.68/) (Hossain, ArabicNLP 2026)
ACL