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

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arxiv 2109.09597 v1 pith:M4KCMUMQ submitted 2021-09-20 cs.CL cs.AIcs.GT

Arkady Arkhangorodsky , Scot Fang , Victoria Knight , Ajay Nagesh , Maria Ryskina , Kevin Knight This is my paper · ORCID
classification cs.CLcs.AIcs.GT
keywords humanapproachesdialogself-playtask-orientedtrainingagent-botsagents
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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.

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