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：法规条文检索（SAR）是指检索与法律问题相关的成文法条任务，是法律文本处理领域颇具前景的应用方向。高质量SAR系统不仅能提升法律专业人员的工作效率，还可为有需要的公民提供免费的基础法律协助。有别于将每份文档视为独立信息源的传统即时检索，SAR处理文本的完整语义依赖于成文法拓扑结构中补足信息的支撑。针对现有研究忽视这些领域特有依赖关系的现状，我们提出新型图增强型密集法规检索器（G-DSR）模型，通过图神经网络融入立法结构特征以提升密集检索性能。实验结果表明，在真实世界专家标注的SAR数据集上，本方法显著优于强基线检索模型。