@inproceedings{emad-eldin-2026-tashkees,
    title = "Tashkees-{AI} at {A}bjad{M}ed 2026: Flat vs. Hierarchical Classification for Fine-Grained {A}rabic Medical {QA}",
    author = "Emad Eldin, Fatimah Mohamed",
    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-1.20/",
    pages = "137--143",
    abstract = "This paper describes Tashkees-AI, a system developed for the AbjadMed 2026 Shared Task on Arabic Medical Question Classification. A comprehensive empirical study was conducted across 82 fine-grained categories, investigating three paradigms: fine-tuned encoder models, hierarchical classification, and ensemble methods. Leveraging a dataset of 27k Arabic medical question-answer pairs, an extensive ablation studies was conducted, comparing MARBERTv2, CAMeLBERT, two-stage hierarchical classifiers, and RAG-based approaches. The findings reveal that fine-tuned MARBERTv2 with data cleaning yields the best performance, achieving a macro F1-score of 0.3659 on the blind test set. In contrast, hierarchical methods surprisingly underperformed (0.332 F1) due to error propagation. The system ranked 26th on the official leaderboard."
}