We address two problems in the field of automatic optimization of dialogue strategies: learning effective dialogue strategies when no initial data or system exists, and evaluating...
This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method...
Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...
Spoken dialogue systemperformance canvary widely fordifferentusers, aswell for the same userduring different dialogues.This paper presents the design and evaluation ofan adaptive v...
We investigate the use of machine learning in combination with feature engineering techniques to explore human multimodal clarification strategies and the use of those strategies ...