@inproceedings{salas-jimenez-etal-2026-aidal,
title = "{AIDAL}-{ARG} at Daleel 2026: Hybrid and Multitask Approaches for {A}rabic Argument Mining",
author = "Salas-Jimenez, Karla and
Mailly, Jean-Guy and
Moudjari, Leila",
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.168/",
pages = "1349--1357",
ISBN = "979-8-89176-497-2",
abstract = "This paper presents our systems for the Daleel 2026 Shared Task. We explore multitask learning, domain-specific ensembles, and a two-stage BIO segmentation and classification pipeline with LLM-based disambiguation, alongside classical machine-learning, Transformer, and LLM baselines. Results show that supervised Transformers outperform standalone LLM prompting, while LLMs are more effective for selectively resolving ambiguous predictions. The multitask model generalizes best on Task 1, achieving 0.599 F1, while the ensemble multitask model reaches 0.683 F1 on Task 2. Our results show the potential of LLM integration within Transformer-based systems for Arabic argumentative discourse mining."
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<abstract>This paper presents our systems for the Daleel 2026 Shared Task. We explore multitask learning, domain-specific ensembles, and a two-stage BIO segmentation and classification pipeline with LLM-based disambiguation, alongside classical machine-learning, Transformer, and LLM baselines. Results show that supervised Transformers outperform standalone LLM prompting, while LLMs are more effective for selectively resolving ambiguous predictions. The multitask model generalizes best on Task 1, achieving 0.599 F1, while the ensemble multitask model reaches 0.683 F1 on Task 2. Our results show the potential of LLM integration within Transformer-based systems for Arabic argumentative discourse mining.</abstract>
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%0 Conference Proceedings
%T AIDAL-ARG at Daleel 2026: Hybrid and Multitask Approaches for Arabic Argument Mining
%A Salas-Jimenez, Karla
%A Mailly, Jean-Guy
%A Moudjari, Leila
%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 salas-jimenez-etal-2026-aidal
%X This paper presents our systems for the Daleel 2026 Shared Task. We explore multitask learning, domain-specific ensembles, and a two-stage BIO segmentation and classification pipeline with LLM-based disambiguation, alongside classical machine-learning, Transformer, and LLM baselines. Results show that supervised Transformers outperform standalone LLM prompting, while LLMs are more effective for selectively resolving ambiguous predictions. The multitask model generalizes best on Task 1, achieving 0.599 F1, while the ensemble multitask model reaches 0.683 F1 on Task 2. Our results show the potential of LLM integration within Transformer-based systems for Arabic argumentative discourse mining.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.168/
%P 1349-1357
Markdown (Informal)
[AIDAL-ARG at Daleel 2026: Hybrid and Multitask Approaches for Arabic Argument Mining](https://aclanthology.org/2026.arabicnlp-sharedtasks.168/) (Salas-Jimenez et al., ArabicNLP 2026)
ACL