Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems

Sarthak Ahuja; Mohammad Kachuee; Fateme Sheikholeslami; Weiqing Liu; Jaeyoung Do

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arXiv · 2305.10528

Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems

Sarthak Ahuja·Mohammad Kachuee·Fateme Sheikholeslami·Weiqing Liu·Jaeyoung Do

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Abstract

Off-Policy reinforcement learning has been a driving force for the state-of-the-art conversational AIs leading to more natural humanagent interactions and improving the user satisfaction for goal-oriented agents. However, in large-scale commercial settings, it is often challenging to balance between policy improvements and experience continuity on the broad spectrum of applications handled by such system. In the literature, off-policy evaluation and guard-railing on aggregate statistics has been commonly used to address this problem. In this paper, we propose a method for curating and leveraging high-precision samples sourced from historical regression incident reports to validate, safe-guard, and improve policies prior to the online deployment. We conducted extensive experiments using data from a real-world conversational system and actual regression incidents. The proposed method is currently deployed in our production system to protect customers against broken experiences and enable long-term policy improvements.

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Sarthak Ahuja, Mohammad Kachuee, Fateme Sheikholeslami, Weiqing Liu, Jaeyoung Do. 2023-05-17. Scalable and Safe Remediation of Defective Actions in Self-Learning Conversational Systems. https://arxiv.org/abs/2305.10528

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