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.
翻译:在以抑郁诊断为主的临床治疗过程中,医生在情感上有充分支持的对话,引导病人根据临床诊断标准暴露其症状。这种对话系统与现有的单一目的的人体机器对话系统有区别,因为它结合了任务导向和聊天以及对话主题和程序的独特性。然而,由于与精神疾病有关的社会耻辱,很少披露与抑郁症咨询和诊断有关的对话数据。根据临床抑郁症诊断标准ICD-11和DSM-5,我们设计了一个三阶段程序,以构建4美元:中国抑郁-诊断-定向聊天对话数据集。该对话系统模拟了诊断抑郁症期间医生和病人之间的对话,包括专业心理医生为每次谈话提供的诊断结果和症状摘要。在新构建的数据集中,确定了反映抑郁症诊断诊断过程的四项任务:反应生成、专题预测、对话摘要以及抑郁症和自杀风险的严重程度分类。多层次的评价结果表明,可以实现一个更具有同情力和诊断力的诊断性诊断性诊断性诊断性诊断性诊断性诊断性诊断性诊断性对话系统,与我们的数据配置性对话系统相比。