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Zero-shot learning in Language & Vision is the task of correctly labelling(or naming) objects of novel categories. Another strand of work in L&V aims atpragmatically informative rather than ``correct'' object descriptions, e.g. inreference games. We combine these lines of research and model zero-shotreference games, where a speaker needs to successfully refer to a novel objectin an image. Inspired by models of "rational speech acts", we extend a neuralgenerator to become a pragmatic speaker reasoning about uncertain objectcategories. As a result of this reasoning, the generator produces fewer nounsand names of distractor categories as compared to a literal speaker. We showthat this conversational strategy for dealing with novel objects often improvescommunicative success, in terms of resolution accuracy of an automaticlistener.

Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories