@inproceedings{t-y-s-s-etal-2024-towards-supporting,
title = "Towards Supporting Legal Argumentation with {NLP}: Is More Data Really All You Need?",
author = "T.y.s.s, Santosh and
Ashley, Kevin and
Atkinson, Katie and
Grabmair, Matthias",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel and
Spanakis, Gerasimos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nllp-1.36",
pages = "404--421",
abstract = "Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI {\&} Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually {``}simpler{'}, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI {\&} Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate conceptual legal knowledge.",
}
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<abstract>Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually “simpler’, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate conceptual legal knowledge.</abstract>
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%0 Conference Proceedings
%T Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
%A T.y.s.s, Santosh
%A Ashley, Kevin
%A Atkinson, Katie
%A Grabmair, Matthias
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F t-y-s-s-etal-2024-towards-supporting
%X Modeling legal reasoning and argumentation justifying decisions in cases has always been central to AI & Law, yet contemporary developments in legal NLP have increasingly focused on statistically classifying legal conclusions from text. While conceptually “simpler’, these approaches often fall short in providing usable justifications connecting to appropriate legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, and distills possibilities of integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. We identify open challenges and discuss the potential of modern NLP models and methods that integrate conceptual legal knowledge.
%U https://aclanthology.org/2024.nllp-1.36
%P 404-421
Markdown (Informal)
[Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?](https://aclanthology.org/2024.nllp-1.36) (T.y.s.s et al., NLLP 2024)
ACL