Attention, intentions, and the structure of discourse
Computational Linguistics
The effect of resource limits and task complexity on collaborative planning in dialogue
Artificial Intelligence - Special volume on empirical methods
ACM Transactions on Computer-Human Interaction (TOCHI)
IEEE Pervasive Computing
Conventions in human-human multi-threaded dialogues: a preliminary study
Proceedings of the 10th international conference on Intelligent user interfaces
Learning mixed initiative dialog strategies by using reinforcement learning on both conversants
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Switching to real-time tasks in multi-tasking dialogue
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Now, where was I? Resumption strategies for an in-vehicle dialogue system
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
An investigation of interruptions and resumptions in multi-tasking dialogues
Computational Linguistics
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In this paper we conduct an exploratory experiment on context restoration in multi-tasking dialogue and report our preliminary findings. We examine a corpus of human-human dialogues, in which pairs of conversants, using speech, work on an ongoing task while occasionally completing real-time tasks. We investigate whether the conversants, when returning to the ongoing task, make any effort to restore the context. First, we identify two types of actions, utterance restatement and information review, as possible restorations. Second, from a statistical analysis, we find that these actions are used more often when returning to the ongoing task, and hence seem to play a role in context restoration. Our findings will help to build a foundation for future speech interfaces that support multi-tasking dialogue.