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        <title>Connecting Symbolic Statutory Reasoning with Legal Information Extraction</title>
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            <namePart type="given">Daniel</namePart>
            <namePart type="family">Preoțiuc-Pietro</namePart>
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            <namePart type="given">Nikolaos</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Connecting Symbolic Statutory Reasoning with Legal Information Extraction
%A Holzenberger, Nils
%A Van Durme, Benjamin
%Y Preoțiuc-Pietro, Daniel
%Y Goanta, Catalina
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Spanakis, Gerasimos
%Y Aletras, Nikolaos
%S Proceedings of the Natural Legal Language Processing Workshop 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F holzenberger-van-durme-2023-connecting
%X 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.
%R 10.18653/v1/2023.nllp-1.12
%U https://aclanthology.org/2023.nllp-1.12/
%U https://doi.org/10.18653/v1/2023.nllp-1.12
%P 113-131

Markdown (Informal)

[Connecting Symbolic Statutory Reasoning with Legal Information Extraction](https://aclanthology.org/2023.nllp-1.12/) (Holzenberger & Van Durme, NLLP 2023)

• Connecting Symbolic Statutory Reasoning with Legal Information Extraction (Holzenberger & Van Durme, NLLP 2023)

ACL

• Nils Holzenberger and Benjamin Van Durme. 2023. Connecting Symbolic Statutory Reasoning with Legal Information Extraction. In Proceedings of the Natural Legal Language Processing Workshop 2023, pages 113–131, Singapore. Association for Computational Linguistics.