@inproceedings{azad-m-san-ahmed-nabi-2026-r,
title = "{R}-{R} at {A}bjad{A}uthor{ID} Shared Task: A Fine-Tuned Approach for {K}urdish Authorship Identification",
author = "Azad M. San Ahmed, Rania and
Nabi, Rebwar M.",
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-1.67/",
pages = "525--529",
abstract = "Authorship identification is a fundamental task in natural language processing and computational stylistics. Despite significant advancements in high-resource languages, lowresource languagesparticularly those utilizing non-Latin scriptsremain largely underexplored, leaving a critical gap in resources and benchmarks for this linguistically distinct, lowresource language. Addressing this oversight, this paper presents Task 3 of AbjadNLP 2026, the first shared task dedicated to authorship identification for Kurdish. The task introduces a newly constructed dataset designed to capture the unique phonological and orthographic features of Sorani Kurdish and formulate the task as a closed-set multiclass classification problem. To establish a robust baseline, we fine-tune the pretrained XLM-RoBERTa model to capture authorial, stylistic patterns. Experimental results on the test set demonstrate the efficacy of transformer-based representations for this domain, achieving an accuracy of approximately 75{\%}."
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%0 Conference Proceedings
%T R-R at AbjadAuthorID Shared Task: A Fine-Tuned Approach for Kurdish Authorship Identification
%A Azad M. San Ahmed, Rania
%A Nabi, Rebwar M.
%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 azad-m-san-ahmed-nabi-2026-r
%X Authorship identification is a fundamental task in natural language processing and computational stylistics. Despite significant advancements in high-resource languages, lowresource languagesparticularly those utilizing non-Latin scriptsremain largely underexplored, leaving a critical gap in resources and benchmarks for this linguistically distinct, lowresource language. Addressing this oversight, this paper presents Task 3 of AbjadNLP 2026, the first shared task dedicated to authorship identification for Kurdish. The task introduces a newly constructed dataset designed to capture the unique phonological and orthographic features of Sorani Kurdish and formulate the task as a closed-set multiclass classification problem. To establish a robust baseline, we fine-tune the pretrained XLM-RoBERTa model to capture authorial, stylistic patterns. Experimental results on the test set demonstrate the efficacy of transformer-based representations for this domain, achieving an accuracy of approximately 75%.
%U https://aclanthology.org/2026.abjadnlp-1.67/
%P 525-529
Markdown (Informal)
[R-R at AbjadAuthorID Shared Task: A Fine-Tuned Approach for Kurdish Authorship Identification](https://aclanthology.org/2026.abjadnlp-1.67/) (Azad M. San Ahmed & Nabi, AbjadNLP 2026)
ACL