@inproceedings{cho-etal-2023-integrative,
title = "An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives",
author = "Cho, Young Min and
Rai, Sunny and
Ungar, Lyle and
Sedoc, Jo{\~a}o and
Guntuku, Sharath",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.698",
doi = "10.18653/v1/2023.emnlp-main.698",
pages = "11346--11369",
abstract = "Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.",
}
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%0 Conference Proceedings
%T An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
%A Cho, Young Min
%A Rai, Sunny
%A Ungar, Lyle
%A Sedoc, João
%A Guntuku, Sharath
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cho-etal-2023-integrative
%X Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.
%R 10.18653/v1/2023.emnlp-main.698
%U https://aclanthology.org/2023.emnlp-main.698
%U https://doi.org/10.18653/v1/2023.emnlp-main.698
%P 11346-11369
Markdown (Informal)
[An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives](https://aclanthology.org/2023.emnlp-main.698) (Cho et al., EMNLP 2023)
ACL