In this paper, we frame zero-shot learning as a challenge for pragmatic modeling and explore zero-shot reference games, where a speaker needs to describe a novel-category object in an image to an addressee who may or may not know the category.
to standard reference games, this game explicitly targets a situation where relatively common words like object names are likely to be more inaccurate than other words like e.g.