  Recent advances in large-scale language modeling and generation have enabled
the creation of dialogue agents that exhibit human-like responses in a wide
range of conversational scenarios spanning a diverse set of tasks, from general
chit-chat to focused goal-oriented discourse. While these agents excel at
generating high-quality responses that are relevant to prior context, they
suffer from a lack of awareness of the overall direction in which the
conversation is headed, and the likelihood of task success inherent therein.
Thus, we propose a framework in which dialogue agents can evaluate the
progression of a conversation toward or away from desired outcomes, and use
this signal to inform planning for subsequent responses. Our framework is
composed of three key elements: (1) the notion of a "global" dialogue state
(GDS) space, (2) a task-specific progression function (PF) computed in terms of
a conversation's trajectory through this space, and (3) a planning mechanism
based on dialogue rollouts by which an agent may use progression signals to
select its next response.
