Enhancing Depression-Diagnosis-Oriented Chat with Psychological State
  Tracking
Yiyang Gu Yougen Zhou Qin Chen Ningning Zhou Jie Zhou Aimin Zhou Liang He
Depression-diagnosis-oriented chat aims to guide patients in self-expression
to collect key symptoms for depression detection. Recent work focuses on
combining task-oriented dialogue and chitchat to simulate the interview-based
depression diagnosis. Whereas, these methods can not well capture the changing
information, feelings, or symptoms of the patient during dialogues. Moreover,
no explicit framework has been explored to guide the dialogue, which results in
some useless communications that affect the experience. In this paper, we
propose to integrate Psychological State Tracking (POST) within the large
language model (LLM) to explicitly guide depression-diagnosis-oriented chat.
Specifically, the state is adapted from a psychological theoretical model,
which consists of four components, namely Stage, Information, Summary and Next.
We fine-tune an LLM model to generate the dynamic psychological state, which is
further used to assist response generation at each turn to simulate the
psychiatrist. Experimental results on the existing benchmark show that our
proposed method boosts the performance of all subtasks in
depression-diagnosis-oriented chat.