@inproceedings{feng-etal-2026-legal,
title = "Legal Judgment Prediction: A Reflection on the State of the Art",
author = "Feng, Yi and
Li, Chuanyi and
Ng, Vincent",
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.2098/",
pages = "45254--45273",
ISBN = "979-8-89176-390-6",
abstract = "Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community because of its practical values in the real world. Significant progress has been achieved on LJP in the past decade. However, most existing LJP research primarily focuses on developing methods that achieve better performance on standard evaluation datasets, with limited emphasis on the long-term advancement of the field beyond improving evaluation metrics. In this position paper, we reflect on the state of the art in LJP research, and explore issues that should motivate researchers to think beyond merely enhancing performance metrics, with the ultimate goal of sparking discussions among LJP researchers about the future trajectory of the field."
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<abstract>Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community because of its practical values in the real world. Significant progress has been achieved on LJP in the past decade. However, most existing LJP research primarily focuses on developing methods that achieve better performance on standard evaluation datasets, with limited emphasis on the long-term advancement of the field beyond improving evaluation metrics. In this position paper, we reflect on the state of the art in LJP research, and explore issues that should motivate researchers to think beyond merely enhancing performance metrics, with the ultimate goal of sparking discussions among LJP researchers about the future trajectory of the field.</abstract>
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%0 Conference Proceedings
%T Legal Judgment Prediction: A Reflection on the State of the Art
%A Feng, Yi
%A Li, Chuanyi
%A Ng, Vincent
%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 feng-etal-2026-legal
%X Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community because of its practical values in the real world. Significant progress has been achieved on LJP in the past decade. However, most existing LJP research primarily focuses on developing methods that achieve better performance on standard evaluation datasets, with limited emphasis on the long-term advancement of the field beyond improving evaluation metrics. In this position paper, we reflect on the state of the art in LJP research, and explore issues that should motivate researchers to think beyond merely enhancing performance metrics, with the ultimate goal of sparking discussions among LJP researchers about the future trajectory of the field.
%U https://aclanthology.org/2026.acl-long.2098/
%P 45254-45273
Markdown (Informal)
[Legal Judgment Prediction: A Reflection on the State of the Art](https://aclanthology.org/2026.acl-long.2098/) (Feng et al., ACL 2026)
ACL
- Yi Feng, Chuanyi Li, and Vincent Ng. 2026. Legal Judgment Prediction: A Reflection on the State of the Art. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45254–45273, San Diego, California, United States. Association for Computational Linguistics.