@inproceedings{aldosari-alshahri-2026-wajeh,
title = "Wajeh at {AISA}-{A}rabic{FC}: A Non-{LLM} Classical {ML} and Retrieval Pipeline for {A}rabic Function Calling",
author = "Aldosari, Abdullah and
Alshahri, Nader",
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.201/",
pages = "1564--1573",
ISBN = "979-8-89176-497-2",
abstract = "We describe NABIQ, the system submitted by team Wajeh (واجه) to Tracks A and B of the AISA-ArabicFC shared task on Arabic function calling. NABIQ contains no large language model, no neural network of any kind, no external API and no GPU. It is a CPU-based pipeline of classical ML, retrieval and rule-based repair: a TF-IDF router, a per-tool nearest-argument retriever over train, span extractors with train-mined gazetteers, and a no-call detector. Its core mechanism is an evidence gate: a candidate value may replace a prediction only if it is a normalised substring of the utterance, a member of a closed canonical map, or a date whose tokens occur in the text. On the blind test the frozen system scored Overall A 0.7904 (rank 16) and Overall B 0.8282 (rank 15) at ThinkRate 1.0000. We keep five evaluation levels distinct because the submitted system drops 0.21 ArgEM from development to test. A rehearsal on 545 held-out training rows, run before the blind release, tracked our official Overall A far better than the tuned development estimate (composite error 0.0033 against 0.1724, cancelling larger FnAcc and ArgEM errors); that contrast, not the components, is our result."
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<abstract>We describe NABIQ, the system submitted by team Wajeh (واجه) to Tracks A and B of the AISA-ArabicFC shared task on Arabic function calling. NABIQ contains no large language model, no neural network of any kind, no external API and no GPU. It is a CPU-based pipeline of classical ML, retrieval and rule-based repair: a TF-IDF router, a per-tool nearest-argument retriever over train, span extractors with train-mined gazetteers, and a no-call detector. Its core mechanism is an evidence gate: a candidate value may replace a prediction only if it is a normalised substring of the utterance, a member of a closed canonical map, or a date whose tokens occur in the text. On the blind test the frozen system scored Overall A 0.7904 (rank 16) and Overall B 0.8282 (rank 15) at ThinkRate 1.0000. We keep five evaluation levels distinct because the submitted system drops 0.21 ArgEM from development to test. A rehearsal on 545 held-out training rows, run before the blind release, tracked our official Overall A far better than the tuned development estimate (composite error 0.0033 against 0.1724, cancelling larger FnAcc and ArgEM errors); that contrast, not the components, is our result.</abstract>
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%0 Conference Proceedings
%T Wajeh at AISA-ArabicFC: A Non-LLM Classical ML and Retrieval Pipeline for Arabic Function Calling
%A Aldosari, Abdullah
%A Alshahri, Nader
%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 aldosari-alshahri-2026-wajeh
%X We describe NABIQ, the system submitted by team Wajeh (واجه) to Tracks A and B of the AISA-ArabicFC shared task on Arabic function calling. NABIQ contains no large language model, no neural network of any kind, no external API and no GPU. It is a CPU-based pipeline of classical ML, retrieval and rule-based repair: a TF-IDF router, a per-tool nearest-argument retriever over train, span extractors with train-mined gazetteers, and a no-call detector. Its core mechanism is an evidence gate: a candidate value may replace a prediction only if it is a normalised substring of the utterance, a member of a closed canonical map, or a date whose tokens occur in the text. On the blind test the frozen system scored Overall A 0.7904 (rank 16) and Overall B 0.8282 (rank 15) at ThinkRate 1.0000. We keep five evaluation levels distinct because the submitted system drops 0.21 ArgEM from development to test. A rehearsal on 545 held-out training rows, run before the blind release, tracked our official Overall A far better than the tuned development estimate (composite error 0.0033 against 0.1724, cancelling larger FnAcc and ArgEM errors); that contrast, not the components, is our result.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.201/
%P 1564-1573
Markdown (Informal)
[Wajeh at AISA-ArabicFC: A Non-LLM Classical ML and Retrieval Pipeline for Arabic Function Calling](https://aclanthology.org/2026.arabicnlp-sharedtasks.201/) (Aldosari & Alshahri, ArabicNLP 2026)
ACL