  Conversational Agents (CAs) powered with deep language models (DLMs) have
shown tremendous promise in the domain of mental health. Prominently, the CAs
have been used to provide informational or therapeutic services to patients.
However, the utility of CAs to assist in mental health triaging has not been
explored in the existing work as it requires a controlled generation of
follow-up questions (FQs), which are often initiated and guided by the mental
health professionals (MHPs) in clinical settings. In the context of depression,
our experiments show that DLMs coupled with process knowledge in a mental
health questionnaire generate 12.54% and 9.37% better FQs based on similarity
and longest common subsequence matches to questions in the PHQ-9 dataset
respectively, when compared with DLMs without process knowledge support.
Despite coupling with process knowledge, we find that DLMs are still prone to
hallucination, i.e., generating redundant, irrelevant, and unsafe FQs. We
demonstrate the challenge of using existing datasets to train a DLM for
generating FQs that adhere to clinical process knowledge. To address this
limitation, we prepared an extended PHQ-9 based dataset, PRIMATE, in
collaboration with MHPs. PRIMATE contains annotations regarding whether a
particular question in the PHQ-9 dataset has already been answered in the
user's initial description of the mental health condition. We used PRIMATE to
train a DLM in a supervised setting to identify which of the PHQ-9 questions
can be answered directly from the user's post and which ones would require more
information from the user. Using performance analysis based on MCC scores, we
show that PRIMATE is appropriate for identifying questions in PHQ-9 that could
guide generative DLMs towards controlled FQ generation suitable for aiding
triaging. Dataset created as a part of this research:
https://github.com/primate-mh/Primate2022
