Balancing Efficiency and Coverage in Human-Robot Dialogue Collection

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 ut...

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Bibliographic Details
Published in:arXiv.org 2018-10
Main Authors: Matthew Marge, Bonial, Claire, Lukin, Stephanie, Hayes, Cory, Foots, Ashley, Artstein, Ron, Cassidy, Henry, Pollard, Kimberly, Gordon, Carla, Gervits, Felix, Leuski, Anton, Hill, Susan, Voss, Clare, Traum, David
Format: Article
Language:English
Subjects:
Collaboration
Data acquisition
Data collection
Graphical user interface
Interactive learning
Navigation
Robots
Online Access:Get full text
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Summary: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.
ISSN:2331-8422