In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user's text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received.

翻译：本研究提出一个新型数据集及计算策略，用于构建旨在引导用户实践自我依恋疗法方案的数字化教练。该框架通过整合基于规则的对话代理与深度学习分类器，实现对用户文本回复中潜在情绪的识别，同时采用深度学习辅助检索方法生成新颖、流畅且具共情性的表述。我们进一步设计了多组拟人化角色供用户选择互动，以期在虚拟治疗会话中实现高参与度。在包含16名参与者的非临床试验中（所有参与者在五天内至少与代理进行了四次交互），我们评估了框架有效性。结果表明，相较于纯规则框架，我们的平台在共情性、用户参与度及实用性方面获得持续更高评价。最后，根据用户反馈为优化应用设计与性能提供了指导方针。