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arXiv · 1810.02017

Balancing Efficiency and Coverage in Human-Robot Dialogue Collection

Matthew Marge·Claire Bonial·Stephanie Lukin·Cory Hayes·Ashley Foots·Ron Artstein·Cassidy Henry·Kimberly Pollard·Carla Gordon·Felix Gervits·Anton Leuski·Susan Hill·Clare Voss·David Traum
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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 towards interactive learning, this work shows that our approach enables the collection of useful training data for navigation-based HRI tasks.

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Matthew Marge, Claire Bonial, Stephanie Lukin, Cory Hayes, Ashley Foots, Ron Artstein, Cassidy Henry, Kimberly Pollard, Carla Gordon, Felix Gervits, Anton Leuski, Susan Hill, Clare Voss, David Traum. 2018-10-04. Balancing Efficiency and Coverage in Human-Robot Dialogue Collection. https://arxiv.org/abs/1810.02017

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