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 unsupervised information retrieval methods based on term weighting and pooled embeddings. Our best performing baseline achieves 50.8% R@100, which is promising for the feasibility of the task and indicates that there is still substantial room for improvement. By the specificity of the data domain and addressed task, BSARD presents a unique challenge problem for future research on legal information retrieval.
翻译:法定文章检索是自动检索与法律问题相关的法律条款的任务。虽然在自然语言处理方面最近的进展引起了对许多法律任务的极大兴趣,但由于缺少大规模和高质量的附加说明数据集,法定文章检索基本上没有触及。为解决这一瓶颈问题,我们引入了比利时法定条款检索数据集(BSARD),该数据集由1 100+法国本地法律问题组成,由经验丰富的法学家用22 600+比利时法律条文中的相关条款标注。使用BSARD,我们以术语权重和集合嵌入为基础,将若干未经监督的信息检索方法作为基准。我们的最佳运作基准达到50.8% R@100,这对任务的可行性很有希望,并指出仍有很大的改进空间。根据数据领域和处理的任务的特点,BSARD为今后法律信息检索的研究提出了独特的难题。