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Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play

2021-09-20 14:52:25
Arkady Arkhangorodsky, Scot Fang, Victoria Knight, Ajay Nagesh, Maria Ryskina, Kevin Knight
arXiv_AI
             
Abstract

Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces. However, human/human corpora are frequently too small for supervised training to be effective. This paper investigates two approaches to training agent-bots and user-bots through self-play, in which they autonomously explore an API environment, discovering communication strategies that enable them to solve the task. We give empirical results for both reinforcement learning and game-theoretic equilibrium finding.

Abstract (translated)

URL

https://arxiv.org/abs/2109.09597

PDF

https://arxiv.org/pdf/2109.09597.pdf

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