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对特定案件作出裁决以及律师提出意见的相关案例。这种相关性被称为案例间的引用关系。为了从庞大的案例库中高效地检索相关案例，法律从业者广泛使用检索工具。现有的法律案例检索模型主要通过比较单个案例的文本表示来工作。尽管这些模型取得了不错的检索准确率，但案例间固有的连接关系（如案例引用关系、案例语义关系以及案例法律指控关系）尚未在案例编码中得到充分利用，从而限制了检索性能的进一步改善。在案例库中，存在三种案例连接关系：案例引用关系、案例语义关系和案例法律指控关系。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，本文提出一种基于归纳图学习的CaseLink模型，以利用案例间的固有连接关系进行法律案例检索。该模型引入了一种新颖的全局案例图（Global Case Graph），用于同时表示案例语义关系和案例法律指控关系。此外，还提出了一种带有案例节点度数正则化的对比学习目标，以利用案例引用关系携带的信息来优化模型。在两个基准数据集上进行了大量实验，结果表明CaseLink达到了最先进的性能。相关代码已在 https://github.com/yanran-tang/CaseLink 上发布。