JMS-QA: A Joint Hierarchical Architecture for Mental Health Question Answering

• Published in: IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2024, Vol.32, p.352-363
• Main Authors: Zhao, Yun, Liu, Dexi, Wan, Changxuan, Liu, Xiping, Nie, Jian-yun, Liu, Jiaming
• Format: Article
• Language: English
• Subjects: dataset; Developing countries; latent signal; LDCs; Mental health; neural networks; Question answering (information retrieval); question-answering; Questions; Semantics; Speech processing; Task analysis; Training
• ISSN: 2329-9290; 2329-9304
• DOI: 10.1109/TASLP.2023.3329295

With the increasing scale of mental health problems in modern society, the scarcity of professional assistance is alarming, especially in developing countries. To address this, some online forums have emerged to provide users with useful information and help. However, a user grappling with mental health problems often struggles to find the needed information and assistance on these forums. This is primarily due to the limitations of existing search approaches that often fail to take the characteristics of mental health text into account. In this paper, we propose a new task of mental-health-oriented question-answering (MHQA) which aims to retrieve the appropriate responses for a question post by incorporating the important criteria related to mental health. Our proposed approach, JMS-QA, matches the question post and candidate responses while jointly detecting their latent mental health signals. This enables the method to incorporate mental health signals into its representations. To test the effectiveness of our approach, we create a new dataset for MHQA and conduct experiments on it. The experimental results show that JMS-QA outperforms existing state-of-the-art methods.