@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://preview.aclanthology.org/manual-author-scripts/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{\%}."
}