Tashkees-AI at AbjadMed 2026: Flat vs. Hierarchical Classification for Fine-Grained Arabic Medical QA

Résumé

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 MAR-BERTv2, 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.

Citer ce document

Accès au document

Texte intégral en lecture en ligne, réservé aux abonnés SPHAERO et aux membres de l'institution. Se connecter

Voir l'article sur le site de la revue

Auteur(s)

Statistiques

Consultations : 1

Téléchargements : 0