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    <abstract>Authorship attribution is a critical task in natural language processing with applications ranging from forensic linguistics to plagiarism detection. While well-studied in high-resource languages, it remains challenging for low-resource languages like Arabic and Urdu. In this paper, we present our participation in the AbjadNLP shared task, where we systematically evaluate three distinct approaches: traditional machine learning using SVM with TF-IDF features, fine-tuned transformer-based models (AraBERT), and LLMs. We demonstrate that while fine-tuned AraBERT excels in Arabic, traditional lexical models (SVM) prove more robust for Urdu, outperforming both BERT-based and LLM approaches. We also show that few-shot prompting with LLMs, when operated as a reranker over top candidates, significantly outperforms zero-shot baselines. Our final systems achieved competitive performance, ranking 6th and 1st in the Arabic and Urdu tasks respectively.</abstract>
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%0 Conference Proceedings
%T Uslub at AbjadAuthorID Shared Task: A Comparative Analysis of Traditional Machine Learning and Transformer-Based Models for Authorship Attribution in Arabic and Urdu
%A Alsuhaibani, Shahad
%A Alkaoud, Mohamed
%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 alsuhaibani-alkaoud-2026-uslub
%X Authorship attribution is a critical task in natural language processing with applications ranging from forensic linguistics to plagiarism detection. While well-studied in high-resource languages, it remains challenging for low-resource languages like Arabic and Urdu. In this paper, we present our participation in the AbjadNLP shared task, where we systematically evaluate three distinct approaches: traditional machine learning using SVM with TF-IDF features, fine-tuned transformer-based models (AraBERT), and LLMs. We demonstrate that while fine-tuned AraBERT excels in Arabic, traditional lexical models (SVM) prove more robust for Urdu, outperforming both BERT-based and LLM approaches. We also show that few-shot prompting with LLMs, when operated as a reranker over top candidates, significantly outperforms zero-shot baselines. Our final systems achieved competitive performance, ranking 6th and 1st in the Arabic and Urdu tasks respectively.
%U https://aclanthology.org/2026.abjadnlp-1.65/
%P 515-519

Markdown (Informal)

[Uslub at AbjadAuthorID Shared Task: A Comparative Analysis of Traditional Machine Learning and Transformer-Based Models for Authorship Attribution in Arabic and Urdu](https://aclanthology.org/2026.abjadnlp-1.65/) (Alsuhaibani & Alkaoud, AbjadNLP 2026)

• Uslub at AbjadAuthorID Shared Task: A Comparative Analysis of Traditional Machine Learning and Transformer-Based Models for Authorship Attribution in Arabic and Urdu (Alsuhaibani & Alkaoud, AbjadNLP 2026)

ACL

• Shahad Alsuhaibani and Mohamed Alkaoud. 2026. Uslub at AbjadAuthorID Shared Task: A Comparative Analysis of Traditional Machine Learning and Transformer-Based Models for Authorship Attribution in Arabic and Urdu. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 515–519, Rabat, Morocco. Association for Computational Linguistics.