@inproceedings{park-etal-2023-interactive,
    title = "Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse",
    author = "Park, Jonghyuk  and
      Lascarides, Alex  and
      Ramamoorthy, Subramanian",
    editor = "Amblard, Maxime  and
      Breitholtz, Ellen",
    booktitle = "Proceedings of the 15th International Conference on Computational Semantics",
    month = jun,
    year = "2023",
    address = "Nancy, France",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.iwcs-1.33/",
    pages = "318--331",
    abstract = "Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users. The learner faces a number of significant constraints: learning should be online, incremental and few-shot, as it is expected to perform tangible belief updates right after novel words denoting unforeseen concepts are introduced. In this work, we explore a challenging symbol grounding task{---}discriminating among object classes that look very similar{---}within the constraints imposed by ITL. We demonstrate empirically that more data-efficient grounding results from exploiting the truth-conditions of the teacher{'}s generic statements (e.g., ``Xs have attribute Z.'') and their implicatures in context (e.g., as an answer to ``How are Xs and Ys different?'', one infers Y lacks attribute Z)."
}