Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks

• URL: http://arxiv.org/abs/2301.12847v1
• Date: Mon, 30 Jan 2023 12:59:09 GMT
• Title: Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks
• Authors: Antoine Louis, Gijs van Dijck, Gerasimos Spanakis
• Abstract summary: 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.
• Score: 3.5880535198436156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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