@inproceedings{gheysari-etal-2026-meta,
title = "{M}eta{\_}{L}abs at {S}tance{E}val-2026: Calibrated Routing and Pseudo-Label Self-Training for Cross-Target {A}rabic Stance Detection",
author = "Gheysari, Maryam and
Nazari, Behnam and
Rahimzadeh, Fatemeh",
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.78/",
pages = "617--624",
ISBN = "979-8-89176-497-2",
abstract = "We present CaRPS, our system for Track 1 of StanceEval-2026. CaRPS combines a target-conditioned encoder with an LLM-based labeling and verification process to handle targets that are not seen during encoder fine-tuning. The encoder takes both the target and the tweet as input, while the LLM is used to generate labels and verify difficult predictions. These LLM-generated labels can then be used as pseudo-labeled data to further fine-tune the encoder, with the goal of gradually reducing the need for LLM calls. On the development leaderboard, our target-conditioned encoder alone achieves 87.19{\%} Favg2, 76.56{\%} Favg3, and 85.30{\%} accuracy. For the official evaluation, we used adversarial LLM prompting for the target not seen during encoder fine-tuning, achieving 82.89{\%} Favg2, 62.67{\%} Favg3, and 78.98{\%} accuracy. Additional experiments show that adversarial verification improves stance and sarcasm classification over one-shot prompting, while pseudo-label self-training helps adapt the encoder to a held-out target. Overall, CaRPS explores a practical combination of efficient encoder-based prediction and LLM-based labeling and verification for Arabic stance detection."
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<abstract>We present CaRPS, our system for Track 1 of StanceEval-2026. CaRPS combines a target-conditioned encoder with an LLM-based labeling and verification process to handle targets that are not seen during encoder fine-tuning. The encoder takes both the target and the tweet as input, while the LLM is used to generate labels and verify difficult predictions. These LLM-generated labels can then be used as pseudo-labeled data to further fine-tune the encoder, with the goal of gradually reducing the need for LLM calls. On the development leaderboard, our target-conditioned encoder alone achieves 87.19% Favg2, 76.56% Favg3, and 85.30% accuracy. For the official evaluation, we used adversarial LLM prompting for the target not seen during encoder fine-tuning, achieving 82.89% Favg2, 62.67% Favg3, and 78.98% accuracy. Additional experiments show that adversarial verification improves stance and sarcasm classification over one-shot prompting, while pseudo-label self-training helps adapt the encoder to a held-out target. Overall, CaRPS explores a practical combination of efficient encoder-based prediction and LLM-based labeling and verification for Arabic stance detection.</abstract>
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%0 Conference Proceedings
%T Meta_Labs at StanceEval-2026: Calibrated Routing and Pseudo-Label Self-Training for Cross-Target Arabic Stance Detection
%A Gheysari, Maryam
%A Nazari, Behnam
%A Rahimzadeh, Fatemeh
%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 gheysari-etal-2026-meta
%X We present CaRPS, our system for Track 1 of StanceEval-2026. CaRPS combines a target-conditioned encoder with an LLM-based labeling and verification process to handle targets that are not seen during encoder fine-tuning. The encoder takes both the target and the tweet as input, while the LLM is used to generate labels and verify difficult predictions. These LLM-generated labels can then be used as pseudo-labeled data to further fine-tune the encoder, with the goal of gradually reducing the need for LLM calls. On the development leaderboard, our target-conditioned encoder alone achieves 87.19% Favg2, 76.56% Favg3, and 85.30% accuracy. For the official evaluation, we used adversarial LLM prompting for the target not seen during encoder fine-tuning, achieving 82.89% Favg2, 62.67% Favg3, and 78.98% accuracy. Additional experiments show that adversarial verification improves stance and sarcasm classification over one-shot prompting, while pseudo-label self-training helps adapt the encoder to a held-out target. Overall, CaRPS explores a practical combination of efficient encoder-based prediction and LLM-based labeling and verification for Arabic stance detection.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.78/
%P 617-624
Markdown (Informal)
[Meta_Labs at StanceEval-2026: Calibrated Routing and Pseudo-Label Self-Training for Cross-Target Arabic Stance Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.78/) (Gheysari et al., ArabicNLP 2026)
ACL