@inproceedings{mahari-etal-2024-lepard,
title = "{L}e{P}a{RD}: A Large-Scale Dataset of Judicial Citations to Precedent",
author = "Mahari, Robert and
Stammbach, Dominik and
Ash, Elliott and
Pentland, Alex",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.532",
pages = "9863--9877",
abstract = "We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various approaches on LePaRD, and find that classification-based retrieval appears to work best. Our best models only achieve a recall of 59{\%} when trained on data corresponding to the 10,000 most-cited passages, underscoring the difficulty of legal passage retrieval. By publishing LePaRD, we provide a large-scale and high quality resource to foster further research on legal passage retrieval. We hope that research on this practice-oriented NLP task will help expand access to justice by reducing the burden associated with legal research via computational assistance. Warning: Extracts from judicial opinions may contain offensive language.",
}
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<abstract>We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various approaches on LePaRD, and find that classification-based retrieval appears to work best. Our best models only achieve a recall of 59% when trained on data corresponding to the 10,000 most-cited passages, underscoring the difficulty of legal passage retrieval. By publishing LePaRD, we provide a large-scale and high quality resource to foster further research on legal passage retrieval. We hope that research on this practice-oriented NLP task will help expand access to justice by reducing the burden associated with legal research via computational assistance. Warning: Extracts from judicial opinions may contain offensive language.</abstract>
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%0 Conference Proceedings
%T LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent
%A Mahari, Robert
%A Stammbach, Dominik
%A Ash, Elliott
%A Pentland, Alex
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F mahari-etal-2024-lepard
%X We present the Legal Passage Retrieval Dataset, LePaRD. LePaRD contains millions of examples of U.S. federal judges citing precedent in context. The dataset aims to facilitate work on legal passage retrieval, a challenging practice-oriented legal retrieval and reasoning task. Legal passage retrieval seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various approaches on LePaRD, and find that classification-based retrieval appears to work best. Our best models only achieve a recall of 59% when trained on data corresponding to the 10,000 most-cited passages, underscoring the difficulty of legal passage retrieval. By publishing LePaRD, we provide a large-scale and high quality resource to foster further research on legal passage retrieval. We hope that research on this practice-oriented NLP task will help expand access to justice by reducing the burden associated with legal research via computational assistance. Warning: Extracts from judicial opinions may contain offensive language.
%U https://aclanthology.org/2024.acl-long.532
%P 9863-9877
Markdown (Informal)
[LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent](https://aclanthology.org/2024.acl-long.532) (Mahari et al., ACL 2024)
ACL
- Robert Mahari, Dominik Stammbach, Elliott Ash, and Alex Pentland. 2024. LePaRD: A Large-Scale Dataset of Judicial Citations to Precedent. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9863–9877, Bangkok, Thailand. Association for Computational Linguistics.