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.

通过基于规则的对话代理和深度学习分类器结合的方式，我们提出了一种数字教练配置方案，用于引导用户在自我附着治疗协议方面进行练习，并在虚拟疗法会话中达到高度参与度。在与 N=16 参与者进行的非临床试验中，我们发现与简单的规则基础框架相比，我们的平台在共情性，用户参与度和实用性方面都能得到更好的评价，并提供进一步改进设计和性能的指南。