Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Information state and dialogue management in the TRINDI dialogue move engine toolkit
Natural Language Engineering
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The Knowledge Engineering Review
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
Hi-index | 0.00 |
In recent years reinforcement-learning-based approaches have been widely used for policy optimization in spoken dialogue systems (SDS). A dialogue management policy is a mapping from dialogue states to system actions, i.e. given the state of the dialogue the dialogue policy determines the next action to be performed by the dialogue manager. So-far policy optimization primarily focused on mapping the dialogue state to simple system actions (such as confirm or ask one piece of information) and the possibility of using complex system actions (such as confirm or ask several slots at the same time) has not been well investigated. In this paper we explore the possibilities of using complex (or hybrid) system actions for dialogue management and then discuss the impact of user experience and channel noise on complex action selection. Our experimental results obtained using simulated users reveal that user and noise adaptive hybrid action selection can perform better than dialogue policies which can only perform simple actions.