A Statutory Article Retrieval Dataset in French
Antoine Louis, Gerasimos Spanakis
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/maastrichtlawtech/bsardOfficialIn paperpytorch★ 40
Abstract
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. Our dataset and source code are publicly available.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| BSARD | Two-tower Bi-Encoder (RoBERTa) | Recall@100 | 74.78 | — | Unverified |
| BSARD | Siamese Bi-Encoder (RoBERTa) | Recall@100 | 71.63 | — | Unverified |
| BSARD | BM25 | Recall@100 | 51.33 | — | Unverified |