Towards a Progression-Aware Autonomous Dialogue Agent

Abraham Sanders, Tomek Strzalkowski, Mei Si, Albert Chang, Deepanshu Dey, Jonas Braasch, Dakuo Wang


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.
Anthology ID:
2022.naacl-main.87
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1194–1212
Language:
URL:
https://aclanthology.org/2022.naacl-main.87
DOI:
10.18653/v1/2022.naacl-main.87
Bibkey:
Cite (ACL):
Abraham Sanders, Tomek Strzalkowski, Mei Si, Albert Chang, Deepanshu Dey, Jonas Braasch, and Dakuo Wang. 2022. Towards a Progression-Aware Autonomous Dialogue Agent. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1194–1212, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Towards a Progression-Aware Autonomous Dialogue Agent (Sanders et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.87.pdf