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.

本文提出了一个管道框架来实现可解释的 SCM，该框架标识了包含基本信息的案例特征句子，根据提取的特征句子结果执行类似案例匹配，并对两种情况的特征句子进行对齐以提供用于相似性证据的特征句子。

一个用于相似案例匹配的可解释性框架

An interpretability framework for Similar case matching

In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar
Case Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published
by the Supreme People's Court of China. CAIL2019-SCM focuses on detecting
similar cases, and the participants are required to check which two cases are
more similar in the triplets. There are 711 teams who participated in this
year's competition, and the best team has reached a score of 71.88. We have
also implemented several baselines to help researchers better understand this
task. The dataset and more details can be found from
this https URL

本文介绍了 CAIL2019-SCM（Chinese AI and Law 2019 Similar Case Matching dataset）数据集，包含中国最高人民法院发布的 8964 个案例三元组。该数据集旨在检测相似案例，有 711 支团队参加了今年的比赛，最佳团队的得分为 71.88。我们还实现了几个基线来帮助研究人员更好地了解这个任务。