The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Assessment of dialogue systems by means of a new simulation technique
Speech Communication
The Knowledge Engineering Review
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Simulating the behaviour of older versus younger users when interacting with spoken dialogue systems
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
ITSPOKE: an intelligent tutoring spoken dialogue system
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Comparing user simulation models for dialog strategy learning
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Agenda-based user simulation for bootstrapping a POMDP dialogue system
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Learning factors analysis – a general method for cognitive model evaluation and improvement
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Comparing user simulations for dialogue strategy learning
ACM Transactions on Speech and Language Processing (TSLP)
Assessing user simulation for dialog systems using human judges and automatic evaluation measures
Natural Language Engineering
The video summary GWAP: summarization of videos based on a social game
Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies
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User simulations are shown to be useful in spoken dialog system development. Since most current user simulations deploy probability models to mimic human user behaviors, how to set up user action probabilities in these models is a key problem to solve. One generally used approach is to estimate these probabilities from human user data. However, when building a new dialog system, usually no data or only a small amount of data is available. In this study, we compare estimating user probabilities from a small user data set versus handcrafting the probabilities. We discuss the pros and cons of both solutions for different dialog system development tasks.