@inproceedings{shlkamy-zaghouani-2026-northwestern-9,
title = "Northwestern 9 at {I}slamic{E}val 2026: Fine-Grained Span Detection in {A}rabic Islamic Content Using {CAM}e{LBERT}-Mix",
author = "Shlkamy, Eman Sedqy and
Zaghouani, Wajdi",
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.126/",
pages = "1035--1039",
ISBN = "979-8-89176-497-2",
abstract = "This paper describes our submission to the IslamicEval 2026 Task{~}1 (Span Detection) shared task, which aims to identify and classify evidence spans in AI-generated Arabic Islamic answers. We formulate the task as token-level sequence labeling and fine-tune CAMeLBERT-Mix using BIO tagging, overlapping-window inference, and prediction reconciliation to detect four evidence categories: Ayah, matn, isnad, and claimed{\_}source. On the official development set, our approach achieves a Precision of 95.55{\%}, Recall of 97.06{\%}, F1-score of 96.30{\%}, and Token Accuracy of 98.66{\%}. Multi-seed and qualitative analyses demonstrate the robustness of the proposed framework for Arabic evidence span detection."
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<abstract>This paper describes our submission to the IslamicEval 2026 Task 1 (Span Detection) shared task, which aims to identify and classify evidence spans in AI-generated Arabic Islamic answers. We formulate the task as token-level sequence labeling and fine-tune CAMeLBERT-Mix using BIO tagging, overlapping-window inference, and prediction reconciliation to detect four evidence categories: Ayah, matn, isnad, and claimed_source. On the official development set, our approach achieves a Precision of 95.55%, Recall of 97.06%, F1-score of 96.30%, and Token Accuracy of 98.66%. Multi-seed and qualitative analyses demonstrate the robustness of the proposed framework for Arabic evidence span detection.</abstract>
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%0 Conference Proceedings
%T Northwestern 9 at IslamicEval 2026: Fine-Grained Span Detection in Arabic Islamic Content Using CAMeLBERT-Mix
%A Shlkamy, Eman Sedqy
%A Zaghouani, Wajdi
%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 shlkamy-zaghouani-2026-northwestern-9
%X This paper describes our submission to the IslamicEval 2026 Task 1 (Span Detection) shared task, which aims to identify and classify evidence spans in AI-generated Arabic Islamic answers. We formulate the task as token-level sequence labeling and fine-tune CAMeLBERT-Mix using BIO tagging, overlapping-window inference, and prediction reconciliation to detect four evidence categories: Ayah, matn, isnad, and claimed_source. On the official development set, our approach achieves a Precision of 95.55%, Recall of 97.06%, F1-score of 96.30%, and Token Accuracy of 98.66%. Multi-seed and qualitative analyses demonstrate the robustness of the proposed framework for Arabic evidence span detection.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.126/
%P 1035-1039
Markdown (Informal)
[Northwestern 9 at IslamicEval 2026: Fine-Grained Span Detection in Arabic Islamic Content Using CAMeLBERT-Mix](https://aclanthology.org/2026.arabicnlp-sharedtasks.126/) (Shlkamy & Zaghouani, ArabicNLP 2026)
ACL