A schema-guided approach to dialogue management has been shown in recent work
to be effective in creating robust customizable virtual agents capable of
acting as friendly peers or task assistants. However, successful applications
of these methods in open-ended, mixed-initiative domains remain elusive --
particularly within medical domains such as virtual standardized patients,
where such complex interactions are commonplace -- and require more extensive
and flexible dialogue management capabilities than previous systems provide. In
this paper, we describe a general-purpose schema-guided dialogue management
framework used to develop SOPHIE, a virtual standardized cancer patient that
allows a doctor to conveniently practice for interactions with patients. We
conduct a crowdsourced evaluation of conversations between medical students and
SOPHIE. Our agent is judged to produce responses that are natural, emotionally
appropriate, and consistent with her role as a cancer patient. Furthermore, it
significantly outperforms an end-to-end neural model fine-tuned on a human
standardized patient corpus, attesting to the advantages of a schema-guided
approach.

本文描述了一个通用的基于模式的对话管理框架，用于开发 SOPHIE，一个虚拟的标准癌症患者，以便医生方便地联系患者互动。我们进行了医学生与 SOPHIE 之间的对话众包评估，我们的代理被判定为能够产生自然、情感适当、与她作为癌症患者的角色一致的回应，并且在性能上明显优于在人类标准患者语料库上微调的端到端神经模型，这证明了基于模式的方法的优势。