Evaluating Referring Form Selection Models In Partially-Known Environments
Zhao Han, Polina Rygina, Thomas Williams
Abstract: 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 status (informed by Givenness Hierarchy), and have shown promising results with over 80% accuracy. However, we argue that the task environments lack ecological validity, due to their use of a small number of objects that are constantly activated and easily uniquely identifiable. Accordingly, we present a novel buildingconstruction task that we believe has increased ecological validity. We then show how training cognitive status informed referring form selection models on data collected within this novel task environment yields substantially different results from those found in previous work, providing key insights and directions for future work.