D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat
In a depression-diagnosis-directed clinical session, doctors initiate a
conversation with ample emotional support that guides the patients to expose
their symptoms based on clinical diagnosis criteria. Such a dialogue system is
distinguished from existing single-purpose human-machine dialog systems, as it
combines task-oriented and chit-chats with uniqueness in dialogue topics and
procedures. However, due to the social stigma associated with mental illness,
the dialogue data related to depression consultation and diagnosis are rarely
disclosed. Based on clinical depression diagnostic criteria ICD-11 and DSM-5,
we designed a 3-phase procedure to construct D$^4$: a Chinese Dialogue Dataset
for Depression-Diagnosis-Oriented Chat, which simulates the dialogue between
doctors and patients during the diagnosis of depression, including diagnosis
results and symptom summary given by professional psychiatrists for each
conversation. Upon the newly-constructed dataset, four tasks mirroring the
depression diagnosis process are established: response generation, topic
prediction, dialog summary, and severity classification of depressive episode
and suicide risk. Multi-scale evaluation results demonstrate that a more
empathy-driven and diagnostic-accurate consultation dialogue system trained on
our dataset can be achieved compared to rule-based bots.