Robots must be able to communicate naturally and efficiently, e.g., using concise referring forms like it, that, and the ⟨N’⟩. Recently researchers have started working on Referring Form Selection (RFS) machine learning algorithms but only evaluating them offline using traditional metrics like accuracy. In this work, we investigated how a cognitive status-informed RFS computational… Continue reading Evaluating Cognitive Status-Informed Referring Form Selection for Human-Robot Interactions
Robots that use natural language in collaborative tasks must refer to objects in their environment. Recent work has shown the utility of the linguistic theory of the Givenness Hierarchy (GH) in generating appropriate referring forms. But before referring expression generation, collaborative robots must determine the content and structure of a sequence of utterances, a… Continue reading Givenness Hierarchy Informed Optimal Document Planning for Situated Human-Robot Interaction
This paper won the Best Long-Paper Award at INLG 2022: For autonomous agents such as robots to effectively communicate with humans, they must be able to refer to different entities in situated contexts. In service of this goal, researchers have recently attempted to model the selection of referring forms on the basis of cognitive… Continue reading Evaluating Referring Form Selection Models in Partially-Known Environments
In many domains, robots must be able to communicate to humans through natural language. One of the core capabilities needed for task-based natural language communication is the ability to refer to objects, people, and locations. Existing work on robot referring expression generation has focused nearly exclusively on generation of definite descriptions to visible objects.… Continue reading A Task Design for Studying Referring Behaviors for Linguistic HRI
