CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning

Siddharth Verma, Justin Fu, Sherry Yang, Sergey Levine


Abstract
Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appropriate responses with similar statistical properties. However, dialogue can also be regarded as a goal directed process, where speakers attempt to accomplish a specific task. Reinforcement learning (RL) algorithms are designed specifically for solving such goal-directed problems, but the most direct way to apply RL, through trial-and-error learning in human conversations, is costly. In this paper, we study how offline reinforcement learning can instead be used to train dialogue agents entirely using static datasets collected from human speakers. Our experiments show that recently developed offline RL methods can be combined with language models to yield realistic dialogue agents that better accomplish task goals.
Anthology ID:
2022.naacl-main.332
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:
4471–4491
Language:
URL:
https://aclanthology.org/2022.naacl-main.332
DOI:
10.18653/v1/2022.naacl-main.332
Bibkey:
Cite (ACL):
Siddharth Verma, Justin Fu, Sherry Yang, and Sergey Levine. 2022. CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4471–4491, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning (Verma et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.332.pdf