Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

by   Weijie Yu, et al.

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications – the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely IOT-Match, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.


page 1

page 2

page 3

page 4


An interpretability framework for Similar case matching

Similar Case Matching (SCM) is designed to determine whether two cases a...

ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

An automated system that could assist a judge in predicting the outcome ...

nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models

This paper describes our submission to the Competition on Legal Informat...

Prompt-based Effective Input Reformulation for Legal Case Retrieval

Legal case retrieval plays an important role for legal practitioners to ...

Explaining Legal Concepts with Augmented Large Language Models (GPT-4)

Interpreting the meaning of legal open-textured terms is a key task of l...

CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain

In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Simila...

Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases

Interpretability or explainability is an emerging research field in NLP....

Please sign up or login with your details

Forgot password? Click here to reset