@inproceedings{zarriess-schlangen-2019-know,
    title = "Know What You Don`t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories",
    author = "Zarrie{\ss}, Sina  and
      Schlangen, David",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/P19-1063/",
    doi = "10.18653/v1/P19-1063",
    pages = "654--659",
    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 {\textquotedblleft}correct{\textquotedblright} 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 {\textquotedblleft}rational speech acts{\textquotedblright}, 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."
}