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.
A Statutory Article Retrieval Dataset in French
The introduction of a new dataset for statutory article retrieval benchmarks various retrieval models, finding dense retrieval models to significantly outperform others in the legal domain.
- Year
- 2021
- Venue
- ACL 2022 5
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- 2
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2108.11792v2ARXIV-DEFAULT
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