Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no cost. Unlike traditional ad-hoc information retrieval, where each document is considered a complete source of information, SAR deals with texts whose full sense depends on complementary information from the topological organization of statute law. While existing works ignore these domain-specific dependencies, we propose a novel graph-augmented dense statute retriever (G-DSR) model that incorporates the structure of legislation via a graph neural network to improve dense retrieval performance. Experimental results show that our approach outperforms strong retrieval baselines on a real-world expert-annotated SAR dataset.
翻译:法定条款检索(SAR)是检索与法律问题有关的成文法条款的任务,是法律文本处理的一个很有希望的应用,特别是高质量的SAR系统可以提高法律专业人员的工作效率,免费向有需要的公民提供基本的法律援助。与传统的特设信息检索(每份文件都被视为完整的信息来源)不同,SAR处理的案文完全有意义地取决于成文法的地形组织提供的补充性信息。虽然现有的工作忽视了这些特定领域的依赖性,但我们提出了一个新的图表式密集的法规检索器(G-DSR)模型,通过图形神经网络纳入立法结构,以改善密集检索性能。实验结果表明,我们的方法超过了实际世界专家附加注释的SAR数据集的强大检索基线。