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        <title>Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks</title>
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    <name type="personal">
        <namePart type="given">Antoine</namePart>
        <namePart type="family">Louis</namePart>
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    <name type="personal">
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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            <namePart type="given">Isabelle</namePart>
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    <abstract>Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.</abstract>
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    <identifier type="doi">10.18653/v1/2023.eacl-main.203</identifier>
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        <date>2023-05</date>
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            <start>2761</start>
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%0 Conference Proceedings
%T Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
%A Louis, Antoine
%A van Dijck, Gijs
%A Spanakis, Gerasimos
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F louis-etal-2023-finding
%X Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.
%R 10.18653/v1/2023.eacl-main.203
%U https://aclanthology.org/2023.eacl-main.203
%U https://doi.org/10.18653/v1/2023.eacl-main.203
%P 2761-2776

Markdown (Informal)

[Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks](https://aclanthology.org/2023.eacl-main.203) (Louis et al., EACL 2023)

• Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks (Louis et al., EACL 2023)

ACL

• Antoine Louis, Gijs van Dijck, and Gerasimos Spanakis. 2023. Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2761–2776, Dubrovnik, Croatia. Association for Computational Linguistics.