How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching

TL;DR

This paper introduces LCM-LAI, an end-to-end model that improves legal case retrieval and matching by capturing legal-rational similarity through a multi-task framework and law article-aware attention, outperforming existing methods.

Contribution

The paper presents a novel end-to-end model with a dependent multi-task learning framework and article-aware attention for legal case retrieval and matching, avoiding reliance on expert assumptions.

Findings

Achieves state-of-the-art performance on four real-world datasets.

Effectively captures legal-rational similarity without additional assumptions.

Outperforms conventional semantic similarity methods.

Abstract

Legal case retrieval (LCR) aims to automatically scour for comparable legal cases based on a given query, which is crucial for offering relevant precedents to support the judgment in intelligent legal systems. Due to similar goals, it is often associated with a similar case matching (LCM) task. To address them, a daunting challenge is assessing the uniquely defined legal-rational similarity within the judicial domain, which distinctly deviates from the semantic similarities in general text retrieval. Past works either tagged domain-specific factors or incorporated reference laws to capture legal-rational information. However, their heavy reliance on expert or unrealistic assumptions restricts their practical applicability in real-world scenarios. In this paper, we propose an end-to-end model named LCM-LAI to solve the above challenges. Through meticulous theoretical analysis, LCM-LAI employs a dependent multi-task learning framework to capture legal-rational information within legal cases by a law article prediction (LAP) sub-task, without any additional assumptions in inference. Besides, LCM-LAI proposes an article-aware attention mechanism to evaluate the legal-rational similarity between across-case sentences based on law distribution, which is more effective than conventional semantic similarity. Weperform a series of exhaustive experiments including two different tasks involving four real-world datasets. Results demonstrate that LCM-LAI achieves state-of-the-art performance.