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
Embodied agents for multi-party dialogue in immersive virtual worlds
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Toward a New Generation of Virtual Humans for Interactive Experiences
IEEE Intelligent Systems
Machine Learning
A Study on Prosody and Discourse Structure in Cooperative Dialogues
A Study on Prosody and Discourse Structure in Cooperative Dialogues
The TRAINS 93 Dialogues
Discourse segmentation by human and automated means
Computational Linguistics
Now let's talk about now: identifying cue phrases intonationally
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Investigating cue selection and placement in tutorial discourse
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A prosodic analysis of discourse segments in direction-giving monologues
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
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
Quantitative and qualitative evaluation of Darpa Communicator spoken dialogue systems
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Comparing several aspects of human-computer and human-human dialogues
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Interruption, Resumption and Domain Switching in In-Vehicle Dialogue
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Context restoration in multi-tasking dialogue
Proceedings of the 14th international conference on Intelligent user interfaces
Switching to real-time tasks in multi-tasking dialogue
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Initiative conflicts in task-oriented dialogue
Computer Speech and Language
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
PARADISE-style evaluation of a human-human library corpus
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Improving sentence completion in dialogues with multi-modal features
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Interactions between human---human multi-threaded dialogues and driving
Personal and Ubiquitous Computing
Hi-index | 0.00 |
In this article we focus on human-human multi-tasking dialogues, in which pairs of conversants, using speech, work on an ongoing task while occasionally completing real-time tasks. The ongoing task is a poker game in which conversants need to assemble a poker hand, and the real-time task is a picture game in which conversants need to find out whether they have a certain picture on their displays. We employ empirical corpus studies and machine learning experiments to understand the mechanisms that people use in managing these complex interactions. First, we examine task interruptions: switching from the ongoing task to a real-time task. We find that generally conversants tend to interrupt at a less disruptive context in the ongoing task when possible. We also find that the discourse markers oh and wait occur in initiating a task interruption twice as often as in the conversation of the ongoing task. Pitch is also found to be statistically correlated with task interruptions; in fact, the more disruptive the task interruption, the higher the pitch. Second, we examine task resumptions: returning to the ongoing task after completing an interrupting real-time task. We find that conversants might simply resume the conversation where they left off, but sometimes they repeat the last utterance or summarize the critical information that was exchanged before the interruption. Third, we apply machine learning to determine how well task interruptions can be recognized automatically and to investigate the usefulness of the cues that we find in the corpus studies. We find that discourse context, pitch, and the discourse markers oh and wait are important features to reliably recognize task interruptions; and with non-lexical features one can improve the performance of recognizing task interruptions with more than a 50% relative error reduction over a baseline. Finally, we discuss the implication of our findings for building a speech interface that supports multi-tasking dialogue.