@inproceedings{kanakkassery-2026-sujith,
title = "Sujith Kanakkassery at {A}bjad{M}ed: Imbalance-Aware Transformer Fine-tuning for {A}rabic Medical Text Classification",
author = "Kanakkassery, Sujith",
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://aclanthology.org/2026.abjadnlp-1.48/",
pages = "408--412",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Sujith Kanakkassery at AbjadMed: Imbalance-Aware Transformer Fine-tuning for Arabic Medical Text Classification
%A Kanakkassery, Sujith
%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 kanakkassery-2026-sujith
%X 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.
%U https://aclanthology.org/2026.abjadnlp-1.48/
%P 408-412
Markdown (Informal)
[Sujith Kanakkassery at AbjadMed: Imbalance-Aware Transformer Fine-tuning for Arabic Medical Text Classification](https://aclanthology.org/2026.abjadnlp-1.48/) (Kanakkassery, AbjadNLP 2026)
ACL