Title
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection.
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.
Year
Venue
Field
2018
arXiv: Robotics
Training set,Data collection,Interactive Learning,Pace,Control engineering,Natural language understanding,Human–computer interaction,Engineering,Robot,Wizard,Human–robot interaction
DocType
Volume
Citations 
Journal
abs/1810.02017
0
PageRank 
References 
Authors
0.34
0
14
Name
Order
Citations
PageRank
Matthew Marge126123.90
Claire Bonial223218.02
Stephanie M. Lukin3939.96
Cory J. Hayes453.88
Ashley Foots501.01
Ron Artstein680759.76
Cassidy Henry701.69
Kimberly A. Pollard843.80
Carla Gordon922.42
Felix Gervits1044.38
Anton Leuski1170464.83
Susan G. Hill12385.73
Clare R. Voss1334429.51
David R. Traum141373146.58