An interpretability framework for Similar case matching

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.