How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus
This work addresses the challenge of enabling situated intelligent agents to learn from human teammates through effective questioning, though it is incremental as it focuses on data collection and annotation rather than new methods or broad impacts.
The authors tackled the problem of how intelligent agents should ask questions to learn about novel concepts in physical environments by introducing the Human-Robot Dialogue Learning (HuRDL) Corpus, a novel dialogue dataset collected in a virtual collaborative task setting, which provides annotated data on human question-asking behaviors to improve agent question generation.
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.