@inproceedings{dinh-van-2026-phonghoang,
title = "phonghoang at {S}tance{E}val-2026: A Two-Stage Framework for {A}rabic Stance Detection via Multi-Task Regularization and {LLM}-Guided Data Augmentation",
author = "{\DJ}inh, Ho{\`a}ng Phong and
V{\u{a}}n, Th{\`i}n {\DJ}ặng",
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.73/",
pages = "568--572",
ISBN = "979-8-89176-497-2",
abstract = "This paper describes the system submitted by the phonghoang team for Track 1 (Seen Tar- gets) of the StanceEval-2026 shared task on Arabic stance detection, where models are trained on provided targets and evaluated on a related target within the same thematic do- main. We propose a two-stage framework to address this within-domain target shift. In the first stage, we fine-tune a Multi-Task MAR- BERT model that jointly learns stance, senti- ment, and sarcasm, further regularized with R- Drop and Stochastic Weight Averaging (SWA), achieving Favg2 of 0.8561 on the development set. In the second stage, we adapt to the test target ({``}Women Driving'') by combining con- trastive few-shot pseudo-labeling via a Large Language Model, fusion with an external Ara- bic stance dataset, and high-confidence samples filtered from a thematically related training tar- get. The resulting augmented training set is used to adapt our optimal Stage 1 architecture, achieving an official Favg2 of 0.8431 on the test set."
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<abstract>This paper describes the system submitted by the phonghoang team for Track 1 (Seen Tar- gets) of the StanceEval-2026 shared task on Arabic stance detection, where models are trained on provided targets and evaluated on a related target within the same thematic do- main. We propose a two-stage framework to address this within-domain target shift. In the first stage, we fine-tune a Multi-Task MAR- BERT model that jointly learns stance, senti- ment, and sarcasm, further regularized with R- Drop and Stochastic Weight Averaging (SWA), achieving Favg2 of 0.8561 on the development set. In the second stage, we adapt to the test target (“Women Driving”) by combining con- trastive few-shot pseudo-labeling via a Large Language Model, fusion with an external Ara- bic stance dataset, and high-confidence samples filtered from a thematically related training tar- get. The resulting augmented training set is used to adapt our optimal Stage 1 architecture, achieving an official Favg2 of 0.8431 on the test set.</abstract>
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%0 Conference Proceedings
%T phonghoang at StanceEval-2026: A Two-Stage Framework for Arabic Stance Detection via Multi-Task Regularization and LLM-Guided Data Augmentation
%A Đinh, Hoàng Phong
%A Văn, Thìn Đặng
%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 dinh-van-2026-phonghoang
%X This paper describes the system submitted by the phonghoang team for Track 1 (Seen Tar- gets) of the StanceEval-2026 shared task on Arabic stance detection, where models are trained on provided targets and evaluated on a related target within the same thematic do- main. We propose a two-stage framework to address this within-domain target shift. In the first stage, we fine-tune a Multi-Task MAR- BERT model that jointly learns stance, senti- ment, and sarcasm, further regularized with R- Drop and Stochastic Weight Averaging (SWA), achieving Favg2 of 0.8561 on the development set. In the second stage, we adapt to the test target (“Women Driving”) by combining con- trastive few-shot pseudo-labeling via a Large Language Model, fusion with an external Ara- bic stance dataset, and high-confidence samples filtered from a thematically related training tar- get. The resulting augmented training set is used to adapt our optimal Stage 1 architecture, achieving an official Favg2 of 0.8431 on the test set.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.73/
%P 568-572
Markdown (Informal)
[phonghoang at StanceEval-2026: A Two-Stage Framework for Arabic Stance Detection via Multi-Task Regularization and LLM-Guided Data Augmentation](https://aclanthology.org/2026.arabicnlp-sharedtasks.73/) (Đinh & Văn, ArabicNLP 2026)
ACL