Enhancing Criminal Case Matching through Diverse Legal Factors
<|reference_start|>Enhancing Criminal Case Matching through Diverse Legal Factors: 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.<|reference_end|>
@article{zhao2024enhancing,
    title={Enhancing Criminal Case Matching through Diverse Legal Factors},
    author={Jie Zhao, Ziyu Guan, Wei Zhao, Yue Jiang},
    journal={arXiv preprint arXiv:2406.11172},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11172},
    primaryClass={cs.CL}
}