A Statutory Article Retrieval Dataset in French
This addresses a bottleneck for researchers and practitioners in legal NLP by providing a high-quality dataset for a previously underexplored task, though it is incremental as it builds on existing retrieval methods.
The authors tackled the scarcity of annotated datasets for statutory article retrieval in French by introducing BSARD, a dataset of 1,100+ legal questions labeled with relevant articles, and found that fine-tuned dense retrieval models achieve 74.8% R@100, indicating feasibility but room for improvement.
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.