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Intelligent agents that are confronted with novel concepts in situatedenvironments will need to ask their human teammates questions to learn aboutthe physical world. To better understand this problem, we need data aboutasking questions in situated task-based interactions. To this end, we presentthe Human-Robot Dialogue Learning (HuRDL) Corpus - a novel dialogue corpuscollected in an online interactive virtual environment in which humanparticipants play the role of a robot performing a collaborativetool-organization task. We describe the corpus data and a correspondingannotation scheme to offer insight into the form and content of questions thathumans ask to facilitate learning in a situated environment. We provide thecorpus as an empirically-grounded resource for improving question generation insituated intelligent agents.

How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus