@inproceedings{mahari-etal-2023-law,
title = "The Law and {NLP}: Bridging Disciplinary Disconnects",
author = "Mahari, Robert and
Stammbach, Dominik and
Ash, Elliott and
Pentland, Alex",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.224",
doi = "10.18653/v1/2023.findings-emnlp.224",
pages = "3445--3454",
abstract = "Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. In this position paper, we argue that the slow uptake of NLP in legal practice is exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers. In a review of recent trends in the legal NLP literature, we find limited overlap between the legal NLP community and legal academia. Our interpretation is that some of the most popular legal NLP tasks fail to address the needs of legal practitioners. We discuss examples of legal NLP tasks that promise to bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored.",
}
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<abstract>Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. In this position paper, we argue that the slow uptake of NLP in legal practice is exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers. In a review of recent trends in the legal NLP literature, we find limited overlap between the legal NLP community and legal academia. Our interpretation is that some of the most popular legal NLP tasks fail to address the needs of legal practitioners. We discuss examples of legal NLP tasks that promise to bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored.</abstract>
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%0 Conference Proceedings
%T The Law and NLP: Bridging Disciplinary Disconnects
%A Mahari, Robert
%A Stammbach, Dominik
%A Ash, Elliott
%A Pentland, Alex
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mahari-etal-2023-law
%X Legal practice is intrinsically rooted in the fabric of language, yet legal practitioners and scholars have been slow to adopt tools from natural language processing (NLP). At the same time, the legal system is experiencing an access to justice crisis, which could be partially alleviated with NLP. In this position paper, we argue that the slow uptake of NLP in legal practice is exacerbated by a disconnect between the needs of the legal community and the focus of NLP researchers. In a review of recent trends in the legal NLP literature, we find limited overlap between the legal NLP community and legal academia. Our interpretation is that some of the most popular legal NLP tasks fail to address the needs of legal practitioners. We discuss examples of legal NLP tasks that promise to bridge disciplinary disconnects and highlight interesting areas for legal NLP research that remain underexplored.
%R 10.18653/v1/2023.findings-emnlp.224
%U https://aclanthology.org/2023.findings-emnlp.224
%U https://doi.org/10.18653/v1/2023.findings-emnlp.224
%P 3445-3454
Markdown (Informal)
[The Law and NLP: Bridging Disciplinary Disconnects](https://aclanthology.org/2023.findings-emnlp.224) (Mahari et al., Findings 2023)
ACL
- Robert Mahari, Dominik Stammbach, Elliott Ash, and Alex Pentland. 2023. The Law and NLP: Bridging Disciplinary Disconnects. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 3445–3454, Singapore. Association for Computational Linguistics.