DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation

Seungyeon Seo, Gary Geunbae Lee


Abstract
Dialogue systems for mental health care aim to provide appropriate support to individuals experiencing mental distress. While extensive research has been conducted to deliver adequate emotional support, existing studies cannot identify individuals who require professional medical intervention and cannot offer suitable guidance. We introduce the Diagnostic Emotional Support Conversation task for an advanced mental health management system. We develop the DESC dataset to assess depression symptoms while maintaining user experience by utilizing task-specific utterance generation prompts and a strict filtering algorithm. Evaluations by professional psychological counselors indicate that DESC has a superior ability to diagnose depression than existing data. Additionally, conversational quality evaluation reveals that DESC maintains fluent, consistent, and coherent dialogues.
Anthology ID:
2024.sigdial-1.59
Original:
2024.sigdial-1.59v1
Version 2:
2024.sigdial-1.59v2
Volume:
Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Month:
September
Year:
2024
Address:
Kyoto, Japan
Editors:
Tatsuya Kawahara, Vera Demberg, Stefan Ultes, Koji Inoue, Shikib Mehri, David Howcroft, Kazunori Komatani
Venue:
SIGDIAL
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
686–698
Language:
URL:
https://aclanthology.org/2024.sigdial-1.59
DOI:
10.18653/v1/2024.sigdial-1.59
Bibkey:
Cite (ACL):
Seungyeon Seo and Gary Geunbae Lee. 2024. DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 686–698, Kyoto, Japan. Association for Computational Linguistics.
Cite (Informal):
DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation (Seo & Lee, SIGDIAL 2024)
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PDF:
https://preview.aclanthology.org/landing_page/2024.sigdial-1.59.pdf