Year: 2022
Type: preprint
Abstract: Despite recent advances in natural language understanding and generation, and decades of research on the development of conversational bots, building automated agents that can carry on rich open-ended conversations with humans "in the wild" remains a formidable challenge. In this work we develop a real-time, open-ended dialogue system that uses reinforcement learning (RL) to power a bot's conversational skill at scale. Our work pairs the succinct embedding of the conversation state generated using SOTA (supervised) language models with RL techniques that are particularly suited to a dynamic action space that changes as the conversation progresses. Trained using crowd-sourced data, our novel system is able to substantially exceeds the (strong) baseline supervised model with respect to several metrics of interest in a live experiment with real users of the Google Assistant.
Source: arXiv (Cornell University)
Authors: Cohen, Deborah, Ryu, Moonkyung, Chow, Yinlam, Keller, Orgad, Greenberg, Ido, Hassidim, Avinatan, Fink, Michael, Matias, Yossi, Szpektor, Idan, Boutilier, Craig, Elidan, Gal
Institution: Google (United States)
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4290062243