Know what you don't know: Modeling a pragmatic speaker that refers to objects of unknown categories

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Abstract

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.

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APA

Zarrieß, S., & Schlangen, D. (2020). Know what you don’t know: Modeling a pragmatic speaker that refers to objects of unknown categories. In ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 654–659). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p19-1063

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