@inproceedings{prus-etal-2025-world,
    title = "World Knowledge Resolves Some Aspectual Ambiguity",
    author = "Pru{\'s}, Katarzyna  and
      Steedman, Mark  and
      Lopez, Adam",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.findings-acl.683/",
    pages = "13207--13220",
    ISBN = "979-8-89176-256-5",
    abstract = "Annotating event descriptions with their aspectual features is often seen as a pre-requisite to temporal reasoning. However, a recent study by Pru{\'s} et al. (2024) has shown that non-experts' annotations of the aspectual class of English verb phrases can disagree with both expert linguistic annotations and each another. They hypothesised that people use their world knowledge to tacitly conjure their own contexts, leading to disagreement between them. In this paper, we test that hypothesis by adding context to Pru{\'s} et al.{'}s examples and mirroring their experiment. Our results show that whilst their hypothesis explains some of the disagreement, some examples continue to yield divided responses even with the additional context. Finally, we show that outputs from GPT-4, despite to some degree capturing the aspectual class division, are not an accurate predictor of human answers."
}