迈向可靠且具有共情能力的抑郁症诊断导向对话
Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats
AI总结：
本文针对抑郁症诊断对话需兼顾专业性与共情的问题，提出融合任务导向可靠性和共情闲聊的本体定义与生成框架，并在 D$^4$ 上验证其可显著提升任务完成与情感支持生成。
摘要：
聊天机器人可以通过与潜在患者进行交互式对话，成为初步抑郁症诊断的可行工具。然而，诊断相关对话中任务导向与闲聊的融合，需要专业知识和共情能力。这种独特要求对面向单一优化目标的传统对话框架构成了挑战。为解决这一问题，我们提出了一个专为抑郁症诊断对话定制的创新本体定义与生成框架，将任务导向对话的可靠性与共情相关闲聊的吸引力相结合。我们进一步将该框架应用于 D$^4$，这是目前唯一公开的抑郁症诊断导向对话数据集。详尽的实验结果表明，抑郁症诊断中的任务完成和情感支持生成均取得显著提升，从而推动任务导向聊天对话系统开发及其在数字心理健康中应用的更全面方法。
英文摘要：
Chatbots can serve as a viable tool for preliminary depression diagnosis via interactive conversations with potential patients. Nevertheless, the blend of task-oriented and chit-chat in diagnosis-related dialogues necessitates professional expertise and empathy. Such unique requirements challenge traditional dialogue frameworks geared towards single optimization goals. To address this, we propose an innovative ontology definition and generation framework tailored explicitly for depression diagnosis dialogues, combining the reliability of task-oriented conversations with the appeal of empathy-related chit-chat. We further apply the framework to D$^4$, the only existing public dialogue dataset on depression diagnosis-oriented chats. Exhaustive experimental results indicate significant improvements in task completion and emotional support generation in depression diagnosis, fostering a more comprehensive approach to task-oriented chat dialogue system development and its applications in digital mental health.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
