@inproceedings{zhong-etal-2026-implicit,
title = "Implicit Hierarchical Modeling for Cross-System Legal Case Retrieval",
author = "Zhong, Zhengjia and
Lan, Hongyi and
Chen, Yuhong and
Hua, Shengjia and
Liang, Kaiyan and
Li, Xiaodong and
Lin, Xianming and
Li, Hui and
Ji, Rongrong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.853/",
pages = "18053--18074",
ISBN = "979-8-89176-462-0",
abstract = "Legal Case Retrieval (LCR) identifies relevant precedent cases to support legal reasoning and sentencing. A core challenge for cross-system LCR is the structural divergence between legal systems: civil-law systems provide explicit statutory hierarchies but lack inter-case citations, while common-law systems offer rich citation networks but lack uniform codified semantic hierarchies. Existing methods exploit only one side of this divide, remaining system-specific. We propose DSLaw, which unifies both systems by implicitly modeling hierarchical semantics through residual quantization and using the resulting discrete representations as semantic anchors for cross-case graph reasoning. By progressively decomposing legal semantics from coarse concepts to fine-grained distinctions, DSLaw constructs implicit connections even without explicit citations. Experiments on COLIEE2025 (common law) and ELAM (civil law) show DSLaw consistently outperforms strong baselines, while joint training further validates cross-system generalization. The implementation is provided at https://github.com/KDEGroup/DSLaw."
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<abstract>Legal Case Retrieval (LCR) identifies relevant precedent cases to support legal reasoning and sentencing. A core challenge for cross-system LCR is the structural divergence between legal systems: civil-law systems provide explicit statutory hierarchies but lack inter-case citations, while common-law systems offer rich citation networks but lack uniform codified semantic hierarchies. Existing methods exploit only one side of this divide, remaining system-specific. We propose DSLaw, which unifies both systems by implicitly modeling hierarchical semantics through residual quantization and using the resulting discrete representations as semantic anchors for cross-case graph reasoning. By progressively decomposing legal semantics from coarse concepts to fine-grained distinctions, DSLaw constructs implicit connections even without explicit citations. Experiments on COLIEE2025 (common law) and ELAM (civil law) show DSLaw consistently outperforms strong baselines, while joint training further validates cross-system generalization. The implementation is provided at https://github.com/KDEGroup/DSLaw.</abstract>
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%0 Conference Proceedings
%T Implicit Hierarchical Modeling for Cross-System Legal Case Retrieval
%A Zhong, Zhengjia
%A Lan, Hongyi
%A Chen, Yuhong
%A Hua, Shengjia
%A Liang, Kaiyan
%A Li, Xiaodong
%A Lin, Xianming
%A Li, Hui
%A Ji, Rongrong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhong-etal-2026-implicit
%X Legal Case Retrieval (LCR) identifies relevant precedent cases to support legal reasoning and sentencing. A core challenge for cross-system LCR is the structural divergence between legal systems: civil-law systems provide explicit statutory hierarchies but lack inter-case citations, while common-law systems offer rich citation networks but lack uniform codified semantic hierarchies. Existing methods exploit only one side of this divide, remaining system-specific. We propose DSLaw, which unifies both systems by implicitly modeling hierarchical semantics through residual quantization and using the resulting discrete representations as semantic anchors for cross-case graph reasoning. By progressively decomposing legal semantics from coarse concepts to fine-grained distinctions, DSLaw constructs implicit connections even without explicit citations. Experiments on COLIEE2025 (common law) and ELAM (civil law) show DSLaw consistently outperforms strong baselines, while joint training further validates cross-system generalization. The implementation is provided at https://github.com/KDEGroup/DSLaw.
%U https://aclanthology.org/2026.findings-emnlp.853/
%P 18053-18074
Markdown (Informal)
[Implicit Hierarchical Modeling for Cross-System Legal Case Retrieval](https://aclanthology.org/2026.findings-emnlp.853/) (Zhong et al., Findings 2026)
ACL
- Zhengjia Zhong, Hongyi Lan, Yuhong Chen, Shengjia Hua, Kaiyan Liang, Xiaodong Li, Xianming Lin, Hui Li, and Rongrong Ji. 2026. Implicit Hierarchical Modeling for Cross-System Legal Case Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 18053–18074, Budapest, Hungary. Association for Computational Linguistics.