Summarizing natural language database responses
Computational Linguistics
Generating context-sensitive responses to object-related misconceptions
Artificial Intelligence
Participating in explanatory dialogues: interpreting and responding to questions in context
Participating in explanatory dialogues: interpreting and responding to questions in context
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
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
ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics
Experiments in evaluating interactive spoken language systems
HLT '91 Proceedings of the workshop on Speech and Natural Language
HLT '91 Proceedings of the workshop on Speech and Natural Language
A constraint-based model for cooperative response generation in information dialogues
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Towards a Natural Language Driven Automated Help Desk
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Towards developing general models of usability with PARADISE
Natural Language Engineering
MIMIC: an adaptive mixed initiative spoken dialogue system for information queries
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
The CommandTalk spoken dialogue system
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic detection of poor speech recognition at the dialogue level
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Two-way adaptation for robust input interpretation in practical multimodal conversation systems
Proceedings of the 10th international conference on Intelligent user interfaces
Dynamic user level and utility measurement for adaptive dialog in a help-desk system
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
A new taxonomy for the quality of telephone services based on spoken dialogue systems
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Creating a general collaborative dialogue agent with lounge strategy feature
Expert Systems with Applications: An International Journal
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While the notion of a cooperative response has been the focus of considerable research in natural language dialogue systems, there has been little empirical work demonstrating how such responses lead to more efficient, natural, or successful dialogues. This paper presents an experimental evaluation of two alternative response strategies in TOOT, a spoken dialogue agent that allows users to access train schedules stored on the web via a telephone conversation. We compare the performance of two versions of TOOT (literal and cooperative), by having users carry out a set of tasks with each version. By using hypothesis testing methods, we show that a combination of response strategy, application task, and task/strategy interactions account for various types of performance differences. By using the PARADISE evaluation framework to estimate an overall performance function, we identify interdependencies that exist between speech recognition and response strategy. Our results elaborate the conditions under which TOOT's cooperative rather than literal strategy contributes to greater performance.