AraMed: Arabic Medical Question Answering using Pretrained Transformer Language Models

Ashwag Alasmari, Sarah Alhumoud, Waad Alshammari

Abstract
Medical Question Answering systems have gained significant attention in recent years due to their potential to enhance medical decision-making and improve patient care. However, most of the research in this field has focused on English-language datasets, limiting the generalizability of MQA systems to non-English speaking regions. This study introduces AraMed, a large-scale Arabic Medical Question Answering dataset addressing the limited resources available for Arabic medical question answering. AraMed comprises of 270k question-answer pairs based on health consumer questions submitted to online medical forum. Experiments using various deep learning models showcase the dataset’s effectiveness, particularly with AraBERT models achieving highest results, specifically AraBERTv2 obtained an F1 score of 96.73% in the answer selection task. The comparative analysis of different deep learning models provides insights into their strengths and limitations. These findings highlight the potential of AraMed for advancing Arabic medical question answering research and development.
Anthology ID:
2024.osact-1.6
Volume:
Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Hend Al-Khalifa, Kareem Darwish, Hamdy Mubarak, Mona Ali, Tamer Elsayed
Venues:
OSACT | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
50–56
Language:
URL:
https://aclanthology.org/2024.osact-1.6
DOI:
Bibkey:
Cite (ACL):
Ashwag Alasmari, Sarah Alhumoud, and Waad Alshammari. 2024. AraMed: Arabic Medical Question Answering using Pretrained Transformer Language Models. In Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024, pages 50–56, Torino, Italia. ELRA and ICCL.
Cite (Informal):
AraMed: Arabic Medical Question Answering using Pretrained Transformer Language Models (Alasmari et al., OSACT-WS 2024)
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https://preview.aclanthology.org/nschneid-patch-5/2024.osact-1.6.pdf
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