Co-adaptation in Spoken Dialogue Systems

Abstract

Spoken dialogue systems are man-machine interfaces which use speech as the medium of interaction. In recent years, dialogue optimization using reinforcement learning has evolved to be a state-of-the-art technique. The primary focus of research in the dialogue domain is to learn some optimal policy with regard to the task description (reward function) and the user simulation being employed. However, in case of human-human interaction, the parties involved in the dialogue conversation mutually evolve over the period of interaction. This very ability of humans to coadapt attributes largely towards increasing the naturalness of the dialogue. This paper outlines a novel framework for coadaptation in spoken dialogue systems, where the dialogue manager and user simulation evolve over a period of time; they incrementally and mutually optimize their respective behaviors.