  Many practical applications of dialogue technology require the generation of
responses according to a particular developer-specified persona. While a
variety of personas can be elicited from recent large language models, the
opaqueness and unpredictability of these models make it desirable to be able to
specify personas in an explicit form. In previous work, personas have typically
been represented as sets of one-off pieces of self-knowledge that are retrieved
by the dialogue system for use in generation. However, in realistic human
conversations, personas are often revealed through story-like narratives that
involve rich habitual knowledge -- knowledge about kinds of events that an
agent often participates in (e.g., work activities, hobbies, sporting
activities, favorite entertainments, etc.), including typical goals,
sub-events, preconditions, and postconditions of those events. We capture such
habitual knowledge using an explicit schema representation, and propose an
approach to dialogue generation that retrieves relevant schemas to condition a
large language model to generate persona-based responses. Furthermore, we
demonstrate a method for bootstrapping the creation of such schemas by first
generating generic passages from a set of simple facts, and then inducing
schemas from the generated passages.
