@inproceedings{singh-2026-medarabs,
title = "{M}ed{A}rabs at {A}bjad{M}ed: {A}rabic Medical Text Classification via Data- and Algorithm-Level Fusion",
author = "Singh, Amrita",
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-1.12/",
pages = "100--104",
abstract = "In this work, we address the challenges of Arabic medical text classification, focusing on class imbalance and the complexity of the language{'}s morphology. We propose a multiclass classification pipeline based on Data- and Algorithm-Level fusion, which integrates the optimal Back Translation technique for data augmentation with the Class Balanced (CB) loss function to enhance performance. The domain-specific AraBERT model is fine-tuned using this approach, achieving competitive results. On the official test set of the AbjadMed task, our pipeline achieves a Macro-F1 score of 0.4219, and it achieves 0.4068 on the development set."
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<abstract>In this work, we address the challenges of Arabic medical text classification, focusing on class imbalance and the complexity of the language’s morphology. We propose a multiclass classification pipeline based on Data- and Algorithm-Level fusion, which integrates the optimal Back Translation technique for data augmentation with the Class Balanced (CB) loss function to enhance performance. The domain-specific AraBERT model is fine-tuned using this approach, achieving competitive results. On the official test set of the AbjadMed task, our pipeline achieves a Macro-F1 score of 0.4219, and it achieves 0.4068 on the development set.</abstract>
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%0 Conference Proceedings
%T MedArabs at AbjadMed: Arabic Medical Text Classification via Data- and Algorithm-Level Fusion
%A Singh, Amrita
%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 singh-2026-medarabs
%X In this work, we address the challenges of Arabic medical text classification, focusing on class imbalance and the complexity of the language’s morphology. We propose a multiclass classification pipeline based on Data- and Algorithm-Level fusion, which integrates the optimal Back Translation technique for data augmentation with the Class Balanced (CB) loss function to enhance performance. The domain-specific AraBERT model is fine-tuned using this approach, achieving competitive results. On the official test set of the AbjadMed task, our pipeline achieves a Macro-F1 score of 0.4219, and it achieves 0.4068 on the development set.
%U https://aclanthology.org/2026.abjadnlp-1.12/
%P 100-104
Markdown (Informal)
[MedArabs at AbjadMed: Arabic Medical Text Classification via Data- and Algorithm-Level Fusion](https://aclanthology.org/2026.abjadnlp-1.12/) (Singh, AbjadNLP 2026)
ACL