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    <abstract>We present AbjadMed, a shared task on Arabic medical text classification organised as part of the 2nd AbjadNLP workshop at EACL 2026. The task targets supervised multi-class classification under realistic conditions of severe class imbalance, fine-grained category structure, and naturally occurring label noise. Participants assign each Arabic medical question–answer instance to one of 82 predefined categories derived from real healthcare consultations. The dataset is based on the Arabic Healthcare Dataset (AHD) and is released as curated training and test splits containing 27,951 and 18,634 instances respectively, while preserving the original label distribution. Systems are evaluated using macro-averaged F1 to emphasise performance on minority medical topics. Results show that Arabic medical text classification remains challenging even with modern pretrained models, particularly for low-frequency and semantically overlapping categories. AbjadMed provides a reproducible benchmark for studying robustness and generalisation in Arabic healthcare NLP.</abstract>
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%0 Conference Proceedings
%T AbjadMed: Arabic Medical Text Classification at AbjadNLP 2026
%A Gupta, Pranav
%A M, Niranjan Kumar
%A Nagarajan, Balaji
%A Zitouni, Imed
%A El-Haj, Mo
%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 gupta-etal-2026-abjadmed
%X We present AbjadMed, a shared task on Arabic medical text classification organised as part of the 2nd AbjadNLP workshop at EACL 2026. The task targets supervised multi-class classification under realistic conditions of severe class imbalance, fine-grained category structure, and naturally occurring label noise. Participants assign each Arabic medical question–answer instance to one of 82 predefined categories derived from real healthcare consultations. The dataset is based on the Arabic Healthcare Dataset (AHD) and is released as curated training and test splits containing 27,951 and 18,634 instances respectively, while preserving the original label distribution. Systems are evaluated using macro-averaged F1 to emphasise performance on minority medical topics. Results show that Arabic medical text classification remains challenging even with modern pretrained models, particularly for low-frequency and semantically overlapping categories. AbjadMed provides a reproducible benchmark for studying robustness and generalisation in Arabic healthcare NLP.
%U https://aclanthology.org/2026.abjadnlp-1.64/
%P 506-514

Markdown (Informal)

[AbjadMed: Arabic Medical Text Classification at AbjadNLP 2026](https://aclanthology.org/2026.abjadnlp-1.64/) (Gupta et al., AbjadNLP 2026)

• AbjadMed: Arabic Medical Text Classification at AbjadNLP 2026 (Gupta et al., AbjadNLP 2026)

ACL

• Pranav Gupta, Niranjan Kumar M, Balaji Nagarajan, Imed Zitouni, and Mo El-Haj. 2026. AbjadMed: Arabic Medical Text Classification at AbjadNLP 2026. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 506–514, Rabat, Morocco. Association for Computational Linguistics.