Summary of Towards Supporting Legal Argumentation with Nlp: Is More Data Really All You Need?, by T.y.s.s Santosh et al.
Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
by T.Y.S.S Santosh, Kevin D. Ashley, Katie Atkinson, Matthias Grabmair
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Modeling legal reasoning and argumentation justifying decisions in cases has long been a crucial aspect of AI & Law. However, recent developments in legal natural language processing (NLP) have primarily focused on statistically classifying legal conclusions from text. While these approaches are conceptually simpler, they often fall short in providing usable justifications connecting to relevant legal concepts. This paper reviews both traditional symbolic works in AI & Law and recent advances in legal NLP, with the goal of distilling possibilities for integrating expert-informed knowledge to strike a balance between scalability and explanation in symbolic vs. data-driven approaches. The review identifies open challenges and discusses the potential of modern NLP models and methods that integrate symbolic reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can help with making decisions in legal cases. Right now, most AI systems just quickly decide if something is true or not based on what they read. But this doesn’t explain why they made that decision. This paper combines old ideas from computer science and new ideas from language processing to create a better way of using AI for legal decisions. It shows how different approaches can work together to make better decisions, but also points out some challenges that need to be solved. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
