@inproceedings{cai-etal-2026-lexrel,
title = "{L}ex{R}el: Benchmarking Legal Relation Extraction for {C}hinese Civil Cases",
author = "Cai, Yida and
Hu, Ranjuexiao and
Xie, Huiyuan and
Li, Chenyang and
Liu, Yun and
Ye, Yuxiao and
Liu, Zhenghao and
Shen, Weixing and
Liu, Zhiyuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.980/",
pages = "21440--21456",
ISBN = "979-8-89176-390-6",
abstract = "Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases remain underexplored in the field of legal AI, largely due to the absence of comprehensive schemas. In this work, we first introduce a comprehensive schema for legal relations in civil cases, which contains a hierarchical taxonomy and definitions of arguments. Based on this schema, we formulate a legal relation extraction task and present **LexRel**, an expert-annotated benchmark for legal relation extraction in the Chinese civil law domain. We use **LexRel** to evaluate state-of-the-art large language models (LLMs) on legal relation extraction, showing that current LLMs exhibit significant limitations in accurately identifying civil legal relations. Furthermore, we demonstrate that explicitly incorporating information about legal relations leads to promising performance gains on other downstream legal AI tasks."
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<abstract>Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases remain underexplored in the field of legal AI, largely due to the absence of comprehensive schemas. In this work, we first introduce a comprehensive schema for legal relations in civil cases, which contains a hierarchical taxonomy and definitions of arguments. Based on this schema, we formulate a legal relation extraction task and present **LexRel**, an expert-annotated benchmark for legal relation extraction in the Chinese civil law domain. We use **LexRel** to evaluate state-of-the-art large language models (LLMs) on legal relation extraction, showing that current LLMs exhibit significant limitations in accurately identifying civil legal relations. Furthermore, we demonstrate that explicitly incorporating information about legal relations leads to promising performance gains on other downstream legal AI tasks.</abstract>
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%0 Conference Proceedings
%T LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases
%A Cai, Yida
%A Hu, Ranjuexiao
%A Xie, Huiyuan
%A Li, Chenyang
%A Liu, Yun
%A Ye, Yuxiao
%A Liu, Zhenghao
%A Shen, Weixing
%A Liu, Zhiyuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cai-etal-2026-lexrel
%X Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases remain underexplored in the field of legal AI, largely due to the absence of comprehensive schemas. In this work, we first introduce a comprehensive schema for legal relations in civil cases, which contains a hierarchical taxonomy and definitions of arguments. Based on this schema, we formulate a legal relation extraction task and present **LexRel**, an expert-annotated benchmark for legal relation extraction in the Chinese civil law domain. We use **LexRel** to evaluate state-of-the-art large language models (LLMs) on legal relation extraction, showing that current LLMs exhibit significant limitations in accurately identifying civil legal relations. Furthermore, we demonstrate that explicitly incorporating information about legal relations leads to promising performance gains on other downstream legal AI tasks.
%U https://aclanthology.org/2026.acl-long.980/
%P 21440-21456
Markdown (Informal)
[LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases](https://aclanthology.org/2026.acl-long.980/) (Cai et al., ACL 2026)
ACL
- Yida Cai, Ranjuexiao Hu, Huiyuan Xie, Chenyang Li, Yun Liu, Yuxiao Ye, Zhenghao Liu, Weixing Shen, and Zhiyuan Liu. 2026. LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 21440–21456, San Diego, California, United States. Association for Computational Linguistics.