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Computer Science > Computation and Language

arXiv:2406.10974 (cs)
[Submitted on 16 Jun 2024 (v1), last revised 15 Oct 2024 (this version, v3)]

Title:Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?

Authors:T.Y.S.S Santosh, Kevin D. Ashley, Katie Atkinson, Matthias Grabmair
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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
Comments: Accepted to NLLP Workshop, EMNLP 2024
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2406.10974 [cs.CL]
  (or arXiv:2406.10974v3 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2406.10974

Submission history

From: Santosh T.Y.S.S [view email]
[v1] Sun, 16 Jun 2024 15:15:44 UTC (78 KB)
[v2] Sat, 12 Oct 2024 10:22:50 UTC (81 KB)
[v3] Tue, 15 Oct 2024 15:59:34 UTC (81 KB)
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