Towards a Progression-Aware Autonomous Dialogue Agent

TL;DR

This paper introduces a framework for dialogue agents that assesses conversation progression towards goals, enabling more goal-aware responses by modeling dialogue states, progression functions, and response planning.

Contribution

It presents a novel framework incorporating dialogue state modeling, progression evaluation, and planning mechanisms to enhance goal-awareness in autonomous dialogue agents.

Findings

Framework effectively models conversation progression

Agents can plan responses based on progression signals

Improves goal-oriented dialogue performance

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.