Wizard of Oz evaluation of listening-oriented dialogue control using POMDP

We have been working on dialogue control for listening agents. In our previous study [1], we proposed a dialogue control method that maximizes user satisfaction using partially observable Markov decision processes (POMDPs) and evaluated it by a dialogue simulation. We found that it significantly outperforms other stochastic dialogue control methods. However, this result does not necessarily mean that our method works as well in real dialogues with human users. Therefore, in this paper, we evaluate our dialogue control method by a Wizard of Oz (WoZ) experiment. The experimental results show that our POMDP-based method achieves significantly higher user satisfaction than other stochastic models, confirming the validity of our approach. This paper is the first to show the usefulness of POMDP-based dialogue control using human users when the target function is to maximize user satisfaction.