Abstract
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.
Abstract (translated)
聊天机器人可以成为通过与潜在患者进行具有交互性的对话进行初步抑郁诊断的可行工具。然而，在诊断相关的对话中混合了任务导向和闲聊，需要专业知识和同理心。这种独特的需求挑战了针对单一优化目标的傳統對話框架。为了应对这个问题，我们提出了一个专门针对抑郁诊断对话的創新本体定义和生成框架，结合了任务导向对话的可靠性和 empathy 相关的闲聊魅力。我们进一步将该框架应用于 D$^4，这是唯一一个关于抑郁诊断聊天数据的公共对话数据集。完整的实验结果表明，在抑郁诊断中，任务完成和情感支持生成的表现都有显著提高，促进了更全面的任务导向聊天对话系统开发和其在数字心理健康领域的应用。
URL
https://arxiv.org/abs/2404.05012
