Mark Higger and Tom Williams
Companion Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI LBRs)
Referring Expression Generation (REG) is the language generation task of selecting attributes to refer to a target entity. While REG is well-studied in linguistics, its introduction into robotics brings new challenges. For real-world robotic environments, robots may have access to a multitude of irrelevant objects that exist outside the scope of conversation, and traditional REG disambiguates the target referent from all other entities, regardless of relevance. While some newer REG methods take relevance into consideration, they are largely limited to potential referents that are part of the same conversation. In this work, we propose using cognitive statuses to inform the relevance of each entity for REG, narrowing down possible distractors based on cognitive relevance introducing our Givenness Advised Incremental Algorithm (GAIA) which leverages cognitive status for REG. This allows a flexible and enhanced REG, accounting for the context of entities both inside a conversation and within the larger scale environment.