Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
Evaluating discourse understanding in spoken dialogue systems
ACM Transactions on Speech and Language Processing (TSLP)
Training conditional random fields with multivariate evaluation measures
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Predicting the quality and usability of spoken dialogue services
Speech Communication
Business Intelligence from Voice of Customer
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
Analysis of listening-oriented dialogue for building listening agents
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling user satisfaction with Hidden Markov Model
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling and predicting quality in spoken human-computer interaction
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Learning to control listening-oriented dialogue using partially observable markov decision processes
ACM Transactions on Speech and Language Processing (TSLP)
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This paper addresses three important issues in automatic prediction of user satisfaction transitions in dialogues. The first issue concerns the individual differences in user satisfaction ratings and how they affect the possibility of creating a user-independent prediction model. The second issue concerns how to determine appropriate evaluation criteria for predicting user satisfaction transitions. The third issue concerns how to train suitable prediction models. We present our findings for these issues on the basis of the experimental results using dialogue data in two domains.