Sujith Kanakkassery at AbjadMed: Imbalance-Aware Transformer Fine-tuning for Arabic Medical Text Classification

Sujith Kanakkassery


Abstract
This paper describes our system submitted to the AbjadMed 2026 shared task at AbjadNLP. The task focuses on the multi-class classification of Arabic medical texts under severe class imbalance. Our approach fine-tunes a pre-trained Arabic Transformer model and incorporates several imbalance-aware strategies, including data cleaning, class-weighted loss, and label smoothing. Through ablation experiments, we observe consistent improvements over a baseline system, demonstrating the effectiveness of these techniques in improving performance on underrepresented medical categories. Finally, our error analysis highlights persistent challenges related to label sparsity and semantic overlap among medical classes.
Anthology ID:
2026.abjadnlp-1.48
Volume:
Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script
Month:
March
Year:
2026
Address:
Rabat, Morocco
Venues:
AbjadNLP | WS
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Publisher:
Association for Computational Linguistics
Note:
Pages:
408–412
Language:
URL:
https://aclanthology.org/2026.abjadnlp-1.48/
DOI:
Bibkey:
Cite (ACL):
Sujith Kanakkassery. 2026. Sujith Kanakkassery at AbjadMed: Imbalance-Aware Transformer Fine-tuning for Arabic Medical Text Classification. In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 408–412, Rabat, Morocco. Association for Computational Linguistics.
Cite (Informal):
Sujith Kanakkassery at AbjadMed: Imbalance-Aware Transformer Fine-tuning for Arabic Medical Text Classification (Kanakkassery, AbjadNLP 2026)
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