@inproceedings{phu-van-2026-pudydg,
title = "{P}u{D}y{DG} at {AISA}-{A}rabic{FC}: Decomposed Fine-Tuning with Function-Centric Few-Shot Augmentation for {A}rabic Function Calling",
author = "Phu, Duy Dang 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.202/",
pages = "1574--1579",
ISBN = "979-8-89176-497-2",
abstract = "Tool calling has become increasingly important for agentic systems, but remains challenging in multilingual settings, particularly for Arabic. We present our system for the AISA-ArabicFC shared task at ArabicNLP 2026. Our approach combines decomposed multi-task fine-tuning, function-centric few-shot augmentation, and hybrid schema-aware inference. On the development set, our system achieves a function-name accuracy of 1.000 and a normalized argument exact match of 0.858. On the blind test set, the released baseline re-scored with the final scorer obtains a Track A score of 0.8000, compared with 0.9111 for our submission. Our system achieves a function-name accuracy of 0.9973 and an argument exact match score of 0.8536 on this set, ranking seventh in Track A. Our solution is released in the GitHub repository[{\ensuremath{<}}https://github.com/phud774/PuDyDG-AISA-ArabicNLP{\ensuremath{>}}]."
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<abstract>Tool calling has become increasingly important for agentic systems, but remains challenging in multilingual settings, particularly for Arabic. We present our system for the AISA-ArabicFC shared task at ArabicNLP 2026. Our approach combines decomposed multi-task fine-tuning, function-centric few-shot augmentation, and hybrid schema-aware inference. On the development set, our system achieves a function-name accuracy of 1.000 and a normalized argument exact match of 0.858. On the blind test set, the released baseline re-scored with the final scorer obtains a Track A score of 0.8000, compared with 0.9111 for our submission. Our system achieves a function-name accuracy of 0.9973 and an argument exact match score of 0.8536 on this set, ranking seventh in Track A. Our solution is released in the GitHub repository[\ensuremath<https://github.com/phud774/PuDyDG-AISA-ArabicNLP\ensuremath>].</abstract>
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%0 Conference Proceedings
%T PuDyDG at AISA-ArabicFC: Decomposed Fine-Tuning with Function-Centric Few-Shot Augmentation for Arabic Function Calling
%A Phu, Duy Dang
%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 phu-van-2026-pudydg
%X Tool calling has become increasingly important for agentic systems, but remains challenging in multilingual settings, particularly for Arabic. We present our system for the AISA-ArabicFC shared task at ArabicNLP 2026. Our approach combines decomposed multi-task fine-tuning, function-centric few-shot augmentation, and hybrid schema-aware inference. On the development set, our system achieves a function-name accuracy of 1.000 and a normalized argument exact match of 0.858. On the blind test set, the released baseline re-scored with the final scorer obtains a Track A score of 0.8000, compared with 0.9111 for our submission. Our system achieves a function-name accuracy of 0.9973 and an argument exact match score of 0.8536 on this set, ranking seventh in Track A. Our solution is released in the GitHub repository[\ensuremath<https://github.com/phud774/PuDyDG-AISA-ArabicNLP\ensuremath>].
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.202/
%P 1574-1579
Markdown (Informal)
[PuDyDG at AISA-ArabicFC: Decomposed Fine-Tuning with Function-Centric Few-Shot Augmentation for Arabic Function Calling](https://aclanthology.org/2026.arabicnlp-sharedtasks.202/) (Phu & Văn, ArabicNLP 2026)
ACL