@inproceedings{holzenberger-van-durme-2023-connecting,
    title = "Connecting Symbolic Statutory Reasoning with Legal Information Extraction",
    author = "Holzenberger, Nils  and
      Van Durme, Benjamin",
    editor = "Preoțiuc-Pietro, Daniel  and
      Goanta, Catalina  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Spanakis, Gerasimos  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.nllp-1.12/",
    doi = "10.18653/v1/2023.nllp-1.12",
    pages = "113--131",
    abstract = "Statutory reasoning is the task of determining whether a given law {--} a part of a statute {--} applies to a given legal case. Previous work has shown that structured, logical representations of laws and cases can be leveraged to solve statutory reasoning, including on the StAtutory Reasoning Assessment dataset (SARA), but rely on costly human translation into structured representations. Here, we investigate a form of legal information extraction atop the SARA cases, illustrating how the task can be done with high performance. Further, we show how the performance of downstream symbolic reasoning directly correlates with the quality of the information extraction."
}