A Stochastic Model for Computer-Aided Human-Human Dialogue

Abstract

In this paper we introduce a novel model for computer-aided human-human dialogue. In this context, the computer aims at improving the outcome of a human-human task-oriented dialogue by intervening during the course of the interaction. While dialogue state and topic tracking in human-human dialogue have already been studied, few work has been devoted to the sequential part of the problem, where the impact of the system's actions on the future of the conversation is taken into account. This paper addresses this issue by first modelling human-human dialogue as a Markov Reward Process. The task of purposely taking part into the conversation is then optimised within the Linearly Solvable Markov Decision Process framework. Utterances of the Conversational Agent are seen as perturbations in this process, which aim at satisfying the user's long-term goals while keeping the conversation natural. Finally, results obtained by simulation suggest that such an approach is suitable for computer-aided human-human dialogue and is a first step towards three-party dialogue.

Learning dialogue dynamics with the method of moments

“…The ability to provide models of spontaneous human-human dialogue is an important step in understanding dialogue, leading to applications such as goal-oriented human-machine dialogue [1], chatbots [2] computer-aided human-human dialogue [3]. Hidden Markov Models [4] are the traditional framework addressing this problem [5].…”