  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.
