@inproceedings{djamai-etal-2026-namaa-stanceeval,
title = "{NAMAA} at {S}tance{E}val-2026: From Encoder and Decoder Model Fine-Tuning to Data-Centric Retrieval-Augmented In-Context Learning for {A}rabic Stance Detection",
author = "Djamai, Abdelbasset and
Barmandah, Hassan and
Qandos, Nour and
Ahmad, Samar and
Nacar, Omer",
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.74/",
pages = "573--588",
ISBN = "979-8-89176-497-2",
abstract = "Stance detection identifies an author{'}s position toward a specified target, yet Arabic social-media text remains challenging because stance is often implicit and shaped by dialect, sarcasm, informal expression, and culturally specific references. StanceEval-2026 evaluates this problem in two transfer settings: a related target within a seen thematic domain (Track 1) and entirely unseen topics (Track 2). We compare four modeling strategies: single-task Arabic encoder fine-tuning, multi-task learning with sentiment and sarcasm supervision, 4-bit QLoRA adaptation of the ALLaM-7B model, and Retrieval-Augmented In-Context Learning (RAG-ICL) with frozen large language models (LLMs). Our strongest single-backend configuration is a data-centric RAG-ICL pipeline combining label-balanced retrieval, hashtag and negation adaptation, text cleaning, similarity filtering, and target descriptions. A majority-vote ensemble of this configuration across three LLM backends obtains official $F_{avg2}$ scores of 0.8719 on Track 1 and 0.9016 on Track 2, ranking 3rd and 7th, respectively. Under the shared-task test conditions, the retrieval-based systems substantially outperform the supervised systems evaluated here, particularly on Track 2."
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<abstract>Stance detection identifies an author’s position toward a specified target, yet Arabic social-media text remains challenging because stance is often implicit and shaped by dialect, sarcasm, informal expression, and culturally specific references. StanceEval-2026 evaluates this problem in two transfer settings: a related target within a seen thematic domain (Track 1) and entirely unseen topics (Track 2). We compare four modeling strategies: single-task Arabic encoder fine-tuning, multi-task learning with sentiment and sarcasm supervision, 4-bit QLoRA adaptation of the ALLaM-7B model, and Retrieval-Augmented In-Context Learning (RAG-ICL) with frozen large language models (LLMs). Our strongest single-backend configuration is a data-centric RAG-ICL pipeline combining label-balanced retrieval, hashtag and negation adaptation, text cleaning, similarity filtering, and target descriptions. A majority-vote ensemble of this configuration across three LLM backends obtains official F_avg2 scores of 0.8719 on Track 1 and 0.9016 on Track 2, ranking 3rd and 7th, respectively. Under the shared-task test conditions, the retrieval-based systems substantially outperform the supervised systems evaluated here, particularly on Track 2.</abstract>
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%0 Conference Proceedings
%T NAMAA at StanceEval-2026: From Encoder and Decoder Model Fine-Tuning to Data-Centric Retrieval-Augmented In-Context Learning for Arabic Stance Detection
%A Djamai, Abdelbasset
%A Barmandah, Hassan
%A Qandos, Nour
%A Ahmad, Samar
%A Nacar, Omer
%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 djamai-etal-2026-namaa-stanceeval
%X Stance detection identifies an author’s position toward a specified target, yet Arabic social-media text remains challenging because stance is often implicit and shaped by dialect, sarcasm, informal expression, and culturally specific references. StanceEval-2026 evaluates this problem in two transfer settings: a related target within a seen thematic domain (Track 1) and entirely unseen topics (Track 2). We compare four modeling strategies: single-task Arabic encoder fine-tuning, multi-task learning with sentiment and sarcasm supervision, 4-bit QLoRA adaptation of the ALLaM-7B model, and Retrieval-Augmented In-Context Learning (RAG-ICL) with frozen large language models (LLMs). Our strongest single-backend configuration is a data-centric RAG-ICL pipeline combining label-balanced retrieval, hashtag and negation adaptation, text cleaning, similarity filtering, and target descriptions. A majority-vote ensemble of this configuration across three LLM backends obtains official F_avg2 scores of 0.8719 on Track 1 and 0.9016 on Track 2, ranking 3rd and 7th, respectively. Under the shared-task test conditions, the retrieval-based systems substantially outperform the supervised systems evaluated here, particularly on Track 2.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.74/
%P 573-588
Markdown (Informal)
[NAMAA at StanceEval-2026: From Encoder and Decoder Model Fine-Tuning to Data-Centric Retrieval-Augmented In-Context Learning for Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.74/) (Djamai et al., ArabicNLP 2026)
ACL