Assessment of dialogue systems by means of a new simulation technique
Speech Communication
Comparing the utility of state features in spoken dialogue using reinforcement learning
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
User simulation as testing for spoken dialog systems
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Setting up user action probabilities in user simulations for dialog system development
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
A comparison between dialog corpora acquired with real and simulated users
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Semi-automatic creation of resources for spoken dialog systems
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Leveraging hidden dialogue state to select tutorial moves
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Using reinforcement learning to create communication channel management strategies for diverse users
SLPAT '10 Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies
Classifying dialogue in high-dimensional space
ACM Transactions on Speech and Language Processing (TSLP)
Comparing user simulations for dialogue strategy learning
ACM Transactions on Speech and Language Processing (TSLP)
Grammatical error simulation for computer-assisted language learning
Knowledge-Based Systems
Assessing user simulation for dialog systems using human judges and automatic evaluation measures
Natural Language Engineering
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This paper explores what kind of user simulation model is suitable for developing a training corpus for using Markov Decision Processes (MDPs) to automatically learn dialog strategies. Our results suggest that with sparse training data, a model that aims to randomly explore more dialog state spaces with certain constraints actually performs at the same or better than a more complex model that simulates realistic user behaviors in a statistical way.