Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
How quickly should communication robots respond?
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Toward establishing trust in adaptive agents
Proceedings of the 13th international conference on Intelligent user interfaces
Journal of Artificial Intelligence Research
The Emerging Web of Linked Data
IEEE Intelligent Systems
A discourse and dialogue infrastructure for industrial dissemination
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
Towards incremental speech generation in dialogue systems
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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Dialogue-based Question Answering (QA) in the context of information seeking applications is a highly complex user interaction task. QA systems normally include various natural language processing components (i.e., components for question classification and information extraction) and information retrieval components. This paper presents a new approach to equip a multimodal QA system for radiologists with some form of self-knowledge about the expected dialogue processing behaviour and the results themselves. The learned models are used to provide feedback of the QA process, i.e., what the system is doing and delivers as results. The resulting automatic feedback behaviour should enhance the user's trust in the system. To this end, examples of the learned feedback are provided in the context of the generation of system-initiative dialogue feedback to a radiologist's questions.