@inproceedings{a4b4bfbf389b4e2dad4451290096d63c,
title = "Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?",
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.",
author = "Santosh, {T. Y.S.S.} and Ashley, {Kevin D.} and Katie Atkinson and Matthias Grabmair",
note = "Publisher Copyright: {\textcopyright}2024 Association for Computational Linguistics.; 6th Natural Legal Language Processing Workshop 2024, NLLP 2024, co-located with the 2024 Conference on Empirical Methods in Natural Language Processing ; Conference date: 16-11-2024",
year = "2024",
language = "English",
series = "NLLP 2024 - Natural Legal Language Processing Workshop 2024, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "404--421",
editor = "Nikolaos Aletras and Ilias Chalkidis and Leslie Barrett and Catalina Goanta and Daniel Preotiuc-Pietro and Gerasimos Spanakis",
booktitle = "NLLP 2024 - Natural Legal Language Processing Workshop 2024, Proceedings of the Workshop",
}