Similar Case Matching (SCM) is designed to determine whether two cases are similar. The task has an essential role in the legal system, helping legal professionals to find relevant cases quickly and thus deal with them more efficiently. Existing research has focused on improving the model's performance but not on its interpretability. Therefore, this paper proposes a pipeline framework for interpretable SCM, which consists of four modules: a judicial feature sentence identification module, a case matching module, a feature sentence alignment module, and a conflict disambiguation module. Unlike existing SCM methods, our framework will identify feature sentences in a case that contain essential information, perform similar case matching based on the extracted feature sentence results, and align the feature sentences in the two cases to provide evidence for the similarity of the cases. SCM results may conflict with feature sentence alignment results, and our framework further disambiguates against this inconsistency. The experimental results show the effectiveness of our framework, and our work provides a new benchmark for interpretable SCM.

翻译：相似案例匹配（SCM）旨在判断两个案例是否相似。该任务在法律系统中具有重要作用，可帮助法律专业人士快速找到相关案例，从而提高处理效率。现有研究主要侧重于提升模型性能，而非关注其可解释性。为此，本文提出了一种可解释SCM的流水线框架，该框架包含四个模块：司法特征句子识别模块、案例匹配模块、特征句子对齐模块以及冲突消解模块。与现有SCM方法不同，本框架将识别案例中包含关键信息的特征句子，基于提取的特征句子结果进行相似案例匹配，并对两个案例中的特征句子进行对齐，为案例相似性提供证据。SCM结果可能与特征句子对齐结果存在冲突，本框架进一步针对这种不一致性进行消解。实验结果表明了本框架的有效性，我们的工作为可解释SCM提供了新的基准。