Learning effective and engaging strategies for advice-giving human-machine dialogue
Martijn Spitters, Marco De Boni, Jakub Zavrel, Remko Bonnema · Natural Language Engineering · 2008
Abstract We describe a system that automatically learns effective and engaging dialogue strategies, generated from a library of dialogue content, using reinforcement learning from user feedback. Besides the more usual clarification and verification components of dialogue, this library contains various social elements like greetings, apologies, small talk, relational questions and jokes. We tested the method through an experimental dialogue system that encourages take-up of exercise and shows that the learned dialogue policy performs as well as one built by human experts for this system.