Balancing Efficiency and Coverage in Human-Robot Dialogue Collection (bibtex)
by Marge, Matthew, Bonial, Claire, Lukin, Stephanie M., Hayes, Cory J., Foots, Ashley, Artstein, Ron, Henry, Cassidy, Pollard, Kimberly A., Gordon, Carla, Gervits, Felix, Leuski, Anton, Hill, Susan G., Voss, Clare R. and Traum, David
Abstract:
We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towardsinteractivelearning,thisworkshowsthatourapproach enables the collection of useful training data for navigationbased HRI tasks.
Reference:
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection (Marge, Matthew, Bonial, Claire, Lukin, Stephanie M., Hayes, Cory J., Foots, Ashley, Artstein, Ron, Henry, Cassidy, Pollard, Kimberly A., Gordon, Carla, Gervits, Felix, Leuski, Anton, Hill, Susan G., Voss, Clare R. and Traum, David), In Proceedings of the AAAI Fall Symposium on Interactive Learning in Artificial Intelligence for Human-Robot Interaction, arXiv, 2018.
Bibtex Entry:
@inproceedings{marge_balancing_2018,
	address = {Arlington, Virginia},
	title = {Balancing {Efficiency} and {Coverage} in {Human}-{Robot} {Dialogue} {Collection}},
	url = {https://arxiv.org/abs/1810.02017},
	abstract = {We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towardsinteractivelearning,thisworkshowsthatourapproach enables the collection of useful training data for navigationbased HRI tasks.},
	booktitle = {Proceedings of the {AAAI} {Fall} {Symposium} on {Interactive} {Learning} in {Artificial} {Intelligence} for {Human}-{Robot} {Interaction}},
	publisher = {arXiv},
	author = {Marge, Matthew and Bonial, Claire and Lukin, Stephanie M. and Hayes, Cory J. and Foots, Ashley and Artstein, Ron and Henry, Cassidy and Pollard, Kimberly A. and Gordon, Carla and Gervits, Felix and Leuski, Anton and Hill, Susan G. and Voss, Clare R. and Traum, David},
	month = oct,
	year = {2018},
	keywords = {Virtual Humans, UARC, ARL, DoD}
}
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