@inproceedings{abudalfa-etal-2026-abjadauthorid,
title = "{A}bjad{A}uthor{ID}: Authorship Identification for {A}rabic-Script Languages at {A}bjad{NLP} 2026",
author = "Abudalfa, Shadi and
Ezzini, Saad and
Abdelali, Ahmed and
Jarrar, Mustafa and
El-Haj, Mo and
Durrani, Nadir and
Sajjad, Hassan and
Adeeba, Farah and
Ahmadi, Sina",
booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.abjadnlp-1.69/",
pages = "538--544",
abstract = "Authorship identification is a core problem in Natural Language Processing and computational linguistics, with applications spanning digital humanities, literary analysis, and forensic linguistics. While substantial progress has been made for English and other high-resource languages, authorship attribution for languages written in the Arabic (Abjad) script remains underexplored. In this paper, we present an overview of AbjadAuthorID, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which focuses on multiclass authorship identification across Arabic-script languages. The shared task covers Modern Standard Arabic, Urdu, and Kurdish, and is formulated as a closed-set multiclass classification problem over literary text spanning multiple authors and historical periods. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and report official results for the Arabic track. The findings highlight both the effectiveness of current approaches in controlled settings and the challenges posed by lower participation and resource availability in some language tracks. AbjadAuthorID establishes a new benchmark for multilingual authorship attribution in morphologically rich, underrepresented languages."
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<abstract>Authorship identification is a core problem in Natural Language Processing and computational linguistics, with applications spanning digital humanities, literary analysis, and forensic linguistics. While substantial progress has been made for English and other high-resource languages, authorship attribution for languages written in the Arabic (Abjad) script remains underexplored. In this paper, we present an overview of AbjadAuthorID, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which focuses on multiclass authorship identification across Arabic-script languages. The shared task covers Modern Standard Arabic, Urdu, and Kurdish, and is formulated as a closed-set multiclass classification problem over literary text spanning multiple authors and historical periods. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and report official results for the Arabic track. The findings highlight both the effectiveness of current approaches in controlled settings and the challenges posed by lower participation and resource availability in some language tracks. AbjadAuthorID establishes a new benchmark for multilingual authorship attribution in morphologically rich, underrepresented languages.</abstract>
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%0 Conference Proceedings
%T AbjadAuthorID: Authorship Identification for Arabic-Script Languages at AbjadNLP 2026
%A Abudalfa, Shadi
%A Ezzini, Saad
%A Abdelali, Ahmed
%A Jarrar, Mustafa
%A El-Haj, Mo
%A Durrani, Nadir
%A Sajjad, Hassan
%A Adeeba, Farah
%A Ahmadi, Sina
%S Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%F abudalfa-etal-2026-abjadauthorid
%X Authorship identification is a core problem in Natural Language Processing and computational linguistics, with applications spanning digital humanities, literary analysis, and forensic linguistics. While substantial progress has been made for English and other high-resource languages, authorship attribution for languages written in the Arabic (Abjad) script remains underexplored. In this paper, we present an overview of AbjadAuthorID, a shared task organised as part of the AbjadNLP workshop at EACL 2026, which focuses on multiclass authorship identification across Arabic-script languages. The shared task covers Modern Standard Arabic, Urdu, and Kurdish, and is formulated as a closed-set multiclass classification problem over literary text spanning multiple authors and historical periods. We describe the task motivation, dataset construction, evaluation protocol, and participation statistics, and report official results for the Arabic track. The findings highlight both the effectiveness of current approaches in controlled settings and the challenges posed by lower participation and resource availability in some language tracks. AbjadAuthorID establishes a new benchmark for multilingual authorship attribution in morphologically rich, underrepresented languages.
%U https://aclanthology.org/2026.abjadnlp-1.69/
%P 538-544
Markdown (Informal)
[AbjadAuthorID: Authorship Identification for Arabic-Script Languages at AbjadNLP 2026](https://aclanthology.org/2026.abjadnlp-1.69/) (Abudalfa et al., AbjadNLP 2026)
ACL
- Shadi Abudalfa, Saad Ezzini, Ahmed Abdelali, Mustafa Jarrar, Mo El-Haj, Nadir Durrani, Hassan Sajjad, Farah Adeeba, and Sina Ahmadi. 2026. AbjadAuthorID: Authorship Identification for Arabic-Script Languages at AbjadNLP 2026. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 538–544, Rabat, Morocco. Association for Computational Linguistics.