Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
Authors: Santosh T.Y.S.S, Kevin D. Ashley, Katie Atkinson, Matthias Grabmair


Reviewer paper summaries: This paper discusses the integration of legal reasoning and argumentation within AI & Law, contrasting traditional symbolic methods with modern data-driven approaches like NLP. It highlights the limitations of current NLP techniques in providing explainable and conceptually aligned legal conclusions. The focus is on synthesizing expert-informed knowledge with scalable NLP methods to balance explanation and data-driven efficiency. It reviews past AI & Law work, identifies challenges, and explores the potential of combining symbolic reasoning with data-driven models.
Summary of strengths: This paper provides a decent review of the old and new in legal NLP.
Summary of weaknesses: * The question posed in the subtitle ("Is more data really all you need?") is not really answered.
* The paper does not distinguish itself from other reviews of law and nlp, eg Katz et al 2023, Zhong et al, 2020. what is this paper adding? In particular, the argument seems quite similar and overlapping with another recent review paper: Robert Mahari, "The Law and NLP: Bridging Disciplinary Disconnects". But that paper is not cited. And it is not clear what is new in the current paper. So it is hard to assess the contribution.
Comments, suggestions and typos: none
Overall assessment scores: 2