This repository contains the code for reproducing the experimental results presented in the ACL 2022 paper "A Statutory Article Retrieval Dataset in French" by Antoine Louis and Jerry Spanakis.
Statutory article retrieval is the task of automatically retrieving law articles relevant to a legal question. While recent advances in natural language processing have sparked considerable interest in many legal tasks, statutory article retrieval remains primarily untouched due to the scarcity of large-scale and high-quality annotated datasets. To address this bottleneck, we introduce the Belgian Statutory Article Retrieval Dataset (BSARD), which consists of 1,100+ French native legal questions labeled by experienced jurists with relevant articles from a corpus of 22,600+ Belgian law articles. Using BSARD, we benchmark several state-of-the-art retrieval approaches, including lexical and dense architectures, both in zero-shot and supervised setups. We find that fine-tuned dense retrieval models significantly outperform other systems. Our best performing baseline achieves 74.8% R@100, which is promising for the feasibility of the task and indicates there is still room for improvement. By the specificity of the domain and addressed task, BSARD presents a unique challenge problem for future research on legal information retrieval.
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{louis2022statutory,
title = {A Statutory Article Retrieval Dataset in French},
author = {Louis, Antoine and Spanakis, Gerasimos},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
month = may,
year = {2022},
address = {Dublin, Ireland},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2022.acl-long.468},
pages = {6789--6803},
}This repository is MIT-licensed.