In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal case retrieval models mainly work by comparing the text representations of individual cases. Although they obtain a decent retrieval accuracy, the intrinsic case connectivity relationships among cases have not been well exploited for case encoding, therefore limiting the further improvement of retrieval performance. In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship. Due to the inductive manner in the task of legal case retrieval, using case reference as input is not applicable for testing. Thus, in this paper, a CaseLink model based on inductive graph learning is proposed to utilise the intrinsic case connectivity for legal case retrieval, a novel Global Case Graph is incorporated to represent both the case semantic relationship and the case legal charge relationship. A novel contrastive objective with a regularisation on the degree of case nodes is proposed to leverage the information carried by the case reference relationship to optimise the model. Extensive experiments have been conducted on two benchmark datasets, which demonstrate the state-of-the-art performance of CaseLink. The code has been released on https://github.com/yanran-tang/CaseLink.

翻译：在判例法中，先例是用于支持法官判决和律师意见的关联案例，这种关联性被称为案例间引用关系。为高效从大规模案例库中检索相关案例，法律从业者广泛使用检索工具。现有法律案例检索模型主要通过比较单个案例的文本表示来实现检索。尽管这些模型取得了不错的检索精度，但案例间的内在连接关系尚未被充分用于案例编码，从而限制了检索性能的进一步提升。在案例库中，案例间存在三种连接关系：案例引用关系、案例语义关系及案例法律指控关系。由于法律案例检索任务的归纳特性，将案例引用作为输入不适用于测试阶段。因此，本文提出基于归纳式图学习的CaseLink模型，利用案例内在连接关系进行法律案例检索，并引入新型全局案例图来同时表征案例语义关系与案例法律指控关系。我们设计了带案例节点度正则化的对比学习目标，利用案例引用关系所携带的信息优化模型。在两个基准数据集上的大量实验表明，CaseLink达到了最先进的性能。相关代码已发布在https://github.com/yanran-tang/CaseLink。