  Intelligent agents that are confronted with novel concepts in situated
environments will need to ask their human teammates questions to learn about
the physical world. To better understand this problem, we need data about
asking questions in situated task-based interactions. To this end, 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.
