  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 at
pragmatically informative rather than ``correct'' object descriptions, e.g. in
reference games. We combine these lines of research and model zero-shot
reference games, where a speaker needs to successfully refer to a novel object
in an image. Inspired by models of "rational speech acts", we extend a neural
generator to become a pragmatic speaker reasoning about uncertain object
categories. As a result of this reasoning, the generator produces fewer nouns
and names of distractor categories as compared to a literal speaker. We show
that this conversational strategy for dealing with novel objects often improves
communicative success, in terms of resolution accuracy of an automatic
listener.
