  Chatbots can serve as a viable tool for preliminary depression diagnosis via
interactive conversations with potential patients. Nevertheless, the blend of
task-oriented and chit-chat in diagnosis-related dialogues necessitates
professional expertise and empathy. Such unique requirements challenge
traditional dialogue frameworks geared towards single optimization goals. To
address this, we propose an innovative ontology definition and generation
framework tailored explicitly for depression diagnosis dialogues, combining the
reliability of task-oriented conversations with the appeal of empathy-related
chit-chat. We further apply the framework to D$^4$, the only existing public
dialogue dataset on depression diagnosis-oriented chats. Exhaustive
experimental results indicate significant improvements in task completion and
emotional support generation in depression diagnosis, fostering a more
comprehensive approach to task-oriented chat dialogue system development and
its applications in digital mental health.
