@inproceedings{boleda-2017-talking,
    title = "Talking about the world with a distributed model",
    author = "Boleda, Gemma",
    editor = "Alonso, Jose M.  and
      Bugar{\'i}n, Alberto  and
      Reiter, Ehud",
    booktitle = "Proceedings of the 10th International Conference on Natural Language Generation",
    month = sep,
    year = "2017",
    address = "Santiago de Compostela, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/W17-3515/",
    doi = "10.18653/v1/W17-3515",
    pages = "114",
    abstract = "We use language to talk about the world, and so reference is a crucial property of language. However, modeling reference is particularly difficult, as it involves both continuous and discrete as-pects of language. For instance, referring expressions like ``the big mug'' or ``it'' typically contain content words ({``}big'', ``mug''), which are notoriously fuzzy or vague in their meaning, and also fun-ction words ({``}the'', ``it'') that largely serve as discrete pointers. Data-driven, distributed models based on distributional semantics or deep learning excel at the former, but struggle with the latter, and the reverse is true for symbolic models. I present ongoing work on modeling reference with a distribu-ted model aimed at capturing both aspects, and learns to refer directly from reference acts."
}