An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Characterizing and recognizing spoken corrections in human-computer dialogue
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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
Identifying user corrections automatically in spoken dialogue systems
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
An analysis of clarification dialogue for question answering
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
HITIQA: an interactive question answering system a preliminary report
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Experiments with interactive question-answering
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
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To enable conversational QA, it is important to examine key issues addressed in conversational systems in the context of question answering. In conversational systems, understanding user intent is critical to the success of interaction. Recent studies have also shown that the capability to automatically identify problematic situations during interaction can significantly improve the system performance. Therefore, this paper investigates the new implications of user intent and problematic situations in the context of question answering. Our studies indicate that, in basic interactive QA, there are different types of user intent that are tied to different kinds of system performance (e.g., problematic/error free situations). Once users are motivated to find specific information related to their information goals, the interaction context can provide useful cues for the system to automatically identify problematic situations and user intent.