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.
翻译:为了更好地了解这个问题,我们需要有关在基于任务的特定互动中提出问题的数据。为此,我们提供人类-机器人对话学习(HurRDL)Corpus这个在网上互动虚拟环境中收集的新对话材料,在这个环境中,人类参与者扮演机器人的角色,执行协作工具组织的任务。我们描述了有关人身资料和相应的说明计划,以深入了解人类为便利在特定环境中学习而要求解决的问题的形式和内容。我们提供该材料,作为经验基础的资源,用于改善在特定智能物剂中的问题生成。