D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat

TL;DR

This paper introduces D4, a Chinese dialogue dataset for depression diagnosis, enabling the development of empathetic and accurate clinical dialogue systems through four diagnostic tasks.

Contribution

The paper presents a novel Chinese depression diagnosis dialogue dataset and defines four related tasks to improve clinical chatbot systems.

Findings

Multi-scale evaluation shows improved empathy and diagnostic accuracy.

Dataset supports four depression diagnosis tasks.

Empirical results outperform rule-based systems.

Abstract

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.