Towards a Progression-Aware Autonomous Dialogue Agent
- URL: http://arxiv.org/abs/2205.03692v2
- Date: Tue, 10 May 2022 19:10:29 GMT
- Title: Towards a Progression-Aware Autonomous Dialogue Agent
- Authors: Abraham Sanders, Tomek Strzalkowski, Mei Si, Albert Chang, Deepanshu
Dey, Jonas Braasch, Dakuo Wang
- Abstract summary: We propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes.
Our framework is composed of three key elements: (1) the notion of a "global" dialogue state (GDS), (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.
- Score: 14.09591070450606
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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