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A schema-guided approach to dialogue management has been shown in recent workto be effective in creating robust customizable virtual agents capable ofacting as friendly peers or task assistants. However, successful applicationsof 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 extensiveand flexible dialogue management capabilities than previous systems provide. Inthis paper, we describe a general-purpose schema-guided dialogue managementframework used to develop SOPHIE, a virtual standardized cancer patient thatallows a doctor to conveniently practice for interactions with patients. Weconduct a crowdsourced evaluation of conversations between medical students andSOPHIE. Our agent is judged to produce responses that are natural, emotionallyappropriate, and consistent with her role as a cancer patient. Furthermore, itsignificantly outperforms an end-to-end neural model fine-tuned on a humanstandardized patient corpus, attesting to the advantages of a schema-guidedapproach.

A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient