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

TL;DR

This paper introduces the HuRDL Corpus, a new dataset of human-robot dialogues in virtual environments, to analyze how agents should ask questions for effective situated learning.

Contribution

It provides a novel annotated dialogue corpus and analysis scheme to improve question generation strategies for situated intelligent agents.

Findings

The corpus captures diverse question types in collaborative tasks.

Annotations reveal patterns in questions that facilitate learning.

Resource supports development of better question-asking algorithms.

Abstract

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.