We present the Human-Robot Dialogue Learning (HuRDL) Corpus, a novel dialogue corpus collected in an online interactive virtual environment in which human participants play the role of a robot performing a collaborative tool-organization task. We describe the corpus data and a corresponding annotation scheme to offer insight into the form and content of questions that humans ask to facilitate learning in a situated environment. We provide the corpus as an empirically-grounded resource for improving question generation in situated intelligent agents.
@inproceedings{gervitsetal21sigdial,
title={How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus},
author={Felix Gervits and Anthony Roque and Gordon Briggs and Matthias Scheutz and Matthew Marge},
year={2021},
booktitle={Proceedings of SigDial},
url={https://hrilab.tufts.edu/publications/gervitsetal21sigdial.pdf}
}