A Comparative Study of Using Pre-trained Language Models for Mental Healthcare Q&A Classification in Arabic

Ashwag Alasmari, Hassan Alhuzali

Keywords: Mental health, Natural language processing, Question/answer classification, Text classification, Large Language Models

Abstract: This study explores Pre-trained Language Models (PLMs) for Arabic mental health question answering using the novel MentalQA dataset. We establish a baseline for future research and compare PLMs to classical models. Fine-tuned PLMs outperform classical models, with MARBERT achieving the best results (0.89 F1-score). Few-shot learning with GPT models also shows promise. This work highlights PLMs' potential for Arabic mental health applications while identifying areas for further development.

Submission Number: 10