Enhancing Criminal Case Matching through Diverse Legal Factors

TL;DR

This paper introduces a novel two-stage framework that enhances criminal case matching by incorporating diverse legal factors through multi-task learning and an entropy-weighted fusion strategy, significantly improving accuracy over existing methods.

Contribution

The paper proposes DLF-CCM, a two-stage framework that effectively extracts and utilizes diverse legal factors for criminal case matching, addressing previous challenges of manual annotation and factor overlap.

Findings

Significant accuracy improvements over baseline methods.

Effective extraction of legal factors via multi-task learning.

Dynamic fusion of relevance scores enhances matching performance.

Abstract

Criminal case matching endeavors to determine the relevance between different criminal cases. Conventional methods predict the relevance solely based on instance-level semantic features and neglect the diverse legal factors (LFs), which are associated with diverse court judgments. Consequently, comprehensively representing a criminal case remains a challenge for these approaches. Moreover, extracting and utilizing these LFs for criminal case matching face two challenges: (1) the manual annotations of LFs rely heavily on specialized legal knowledge; (2) overlaps among LFs may potentially harm the model's performance. In this paper, we propose a two-stage framework named Diverse Legal Factor-enhanced Criminal Case Matching (DLF-CCM). Firstly, DLF-CCM employs a multi-task learning framework to pre-train an LF extraction network on a large-scale legal judgment prediction dataset. In stage two, DLF-CCM introduces an LF de-redundancy module to learn shared LF and exclusive LFs. Moreover, an entropy-weighted fusion strategy is introduced to dynamically fuse the multiple relevance generated by all LFs. Experimental results validate the effectiveness of DLF-CCM and show its significant improvements over competitive baselines. Code: https://github.com/jiezhao6/DLF-CCM.