This repository contains the code for reproducing the experimental results presented in the EACL 2023 paper "Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks" by Antoine Louis, Gijs van Dijck and Jerry Spanakis.
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.
Detailed documentation on the dataset and how to reproduce the main experimental results can be found here.
For attribution in academic contexts, please cite this work as:
@inproceedings{louis2023finding,
title = {Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks},
author = {Louis, Antoine and van Dijck, Gijs and Spanakis, Gerasimos},
booktitle = {Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics},
month = may,
year = {2023},
address = {Dubrovnik, Croatia},
publisher = {Association for Computational Linguistics},
url = {},
pages = {},
}This repository is licensed under the terms of the CC BY-SA 4.0 license.