  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.
