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.