@inproceedings{li-etal-2025-legal,
title = "Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification",
author = "Li, Ang and
Wu, Yiquan and
Cai, Ming and
Jatowt, Adam and
Zhou, Xiang and
Lu, Weiming and
Sun, Changlong and
Wu, Fei and
Kuang, Kun",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.355/",
doi = "10.18653/v1/2025.naacl-long.355",
pages = "6957--6970",
ISBN = "979-8-89176-189-6",
abstract = "Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. Legal judgments can involve multiple law articles and charges. Although recent methods in LJP have made notable progress, most are constrained to single-task settings (e.g., only predicting charges) or single-label settings (e.g., not accommodating cases with multiple charges), diverging from the complexities of real-world scenarios. In this paper, we address the challenge of predicting relevant law articles and charges within the framework of legal judgment prediction, treating it as a multi-task and multi-label text classification problem. We introduce a knowledge-enhanced approach, called K-LJP, that incorporates (I) ``label-level knowledge'' (such as definitions and relationships among labels) to enhance the representation of case facts for each task, and (ii) ``task-level knowledge'' (such as the alignment between law articles and corresponding charges) to improve task synergy. Comprehensive experiments demonstrate our method{'}s effectiveness in comparison to state-of-the-art (SOTA) baselines."
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<abstract>Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. Legal judgments can involve multiple law articles and charges. Although recent methods in LJP have made notable progress, most are constrained to single-task settings (e.g., only predicting charges) or single-label settings (e.g., not accommodating cases with multiple charges), diverging from the complexities of real-world scenarios. In this paper, we address the challenge of predicting relevant law articles and charges within the framework of legal judgment prediction, treating it as a multi-task and multi-label text classification problem. We introduce a knowledge-enhanced approach, called K-LJP, that incorporates (I) “label-level knowledge” (such as definitions and relationships among labels) to enhance the representation of case facts for each task, and (ii) “task-level knowledge” (such as the alignment between law articles and corresponding charges) to improve task synergy. Comprehensive experiments demonstrate our method’s effectiveness in comparison to state-of-the-art (SOTA) baselines.</abstract>
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%0 Conference Proceedings
%T Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification
%A Li, Ang
%A Wu, Yiquan
%A Cai, Ming
%A Jatowt, Adam
%A Zhou, Xiang
%A Lu, Weiming
%A Sun, Changlong
%A Wu, Fei
%A Kuang, Kun
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F li-etal-2025-legal
%X Legal judgment prediction (LJP) is an essential task for legal AI, aiming at predicting judgments based on the facts of a case. Legal judgments can involve multiple law articles and charges. Although recent methods in LJP have made notable progress, most are constrained to single-task settings (e.g., only predicting charges) or single-label settings (e.g., not accommodating cases with multiple charges), diverging from the complexities of real-world scenarios. In this paper, we address the challenge of predicting relevant law articles and charges within the framework of legal judgment prediction, treating it as a multi-task and multi-label text classification problem. We introduce a knowledge-enhanced approach, called K-LJP, that incorporates (I) “label-level knowledge” (such as definitions and relationships among labels) to enhance the representation of case facts for each task, and (ii) “task-level knowledge” (such as the alignment between law articles and corresponding charges) to improve task synergy. Comprehensive experiments demonstrate our method’s effectiveness in comparison to state-of-the-art (SOTA) baselines.
%R 10.18653/v1/2025.naacl-long.355
%U https://aclanthology.org/2025.naacl-long.355/
%U https://doi.org/10.18653/v1/2025.naacl-long.355
%P 6957-6970
Markdown (Informal)
[Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification](https://aclanthology.org/2025.naacl-long.355/) (Li et al., NAACL 2025)
ACL
- Ang Li, Yiquan Wu, Ming Cai, Adam Jatowt, Xiang Zhou, Weiming Lu, Changlong Sun, Fei Wu, and Kun Kuang. 2025. Legal Judgment Prediction based on Knowledge-enhanced Multi-Task and Multi-Label Text Classification. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 6957–6970, Albuquerque, New Mexico. Association for Computational Linguistics.