Zhao Han and Polina Rygina and Tom Williams
ACL International Conference on Natural Language Generation
Best Long Paper Award
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 Giveness 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 building-construction 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 environments yields substantially different results from those found in previous work, providing key insights and directions for future work.