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

2025-02-25

AI Summary

This paper addresses the challenge of modeling legal rationality similarity in Legal Case Retrieval (LCR) and Legal Case Matching (LCM), where conventional semantic similarity approaches fall short. We propose the first expert-annotation-free, end-to-end method to explicitly model legal reasoning in case precedents. Our approach features: (1) a multi-task learning framework with Law Article Prediction (LAP) as an auxiliary task to implicitly encode statutory grounding; and (2) an article-aware attention mechanism based on article distribution, enabling dependency-free capture of cross-case legal rationality relationships during inference. Evaluated on four real-world legal datasets across both LCR and LCM tasks, our method consistently outperforms state-of-the-art baselines, achieving significant gains in retrieval accuracy and enhancing interpretability of case matches. The results demonstrate its effectiveness in supporting intelligent judicial systems with more reliable and legally grounded precedent recommendations.

Technology Category

• Knowledge Representation and Reasoning: Common-Sense Reasoning
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Automated legal case retrieval
• Judicial domain similarity assessment
• Law article prediction integration

Innovation

• End-to-end model LCM-LAI
• Dependent multi-task learning framework
• Article-aware attention mechanism