@inproceedings{sanders-etal-2022-towards,
title = "Towards a Progression-Aware Autonomous Dialogue Agent",
author = "Sanders, Abraham and
Strzalkowski, Tomek and
Si, Mei and
Chang, Albert and
Dey, Deepanshu and
Braasch, Jonas and
Wang, Dakuo",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.87/",
doi = "10.18653/v1/2022.naacl-main.87",
pages = "1194--1212",
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 {\textquotedblleft}global{\textquotedblright} 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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<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.</abstract>
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%0 Conference Proceedings
%T Towards a Progression-Aware Autonomous Dialogue Agent
%A Sanders, Abraham
%A Strzalkowski, Tomek
%A Si, Mei
%A Chang, Albert
%A Dey, Deepanshu
%A Braasch, Jonas
%A Wang, Dakuo
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F sanders-etal-2022-towards
%X 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.
%R 10.18653/v1/2022.naacl-main.87
%U https://aclanthology.org/2022.naacl-main.87/
%U https://doi.org/10.18653/v1/2022.naacl-main.87
%P 1194-1212
Markdown (Informal)
[Towards a Progression-Aware Autonomous Dialogue Agent](https://aclanthology.org/2022.naacl-main.87/) (Sanders et al., NAACL 2022)
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.