Strategy and Policy Learning for Non-Task-Oriented Conversational Systems

Zhou Yu,Ziyu Xu,A. Black,Alexander I. Rudnicky

Published 2016 in SIGDIAL Conference

ABSTRACT

We propose a set of generic conversational strategies to handle possible sys-tem breakdowns in non-task-oriented dialog systems. We also design dialog policies to select among these strategies with respect to different dialog contexts. We combine expert knowledge and the statistical findings that derived from previous collected data in designing these policies. The dialog policy learned via reinforcement learning outperforms the random selection policy and the locally greedy policy in both the simulated and the real-world settings. In addition, we propose three metrics, which consider both the lo-cal and global quality of the conversation, to evaluate conversation quality.

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