Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?
This work addresses the problem of improving legal argumentation systems for legal professionals by integrating symbolic and data-driven methods, though it is incremental as it reviews and synthesizes existing approaches.
The paper reviews traditional symbolic AI and recent NLP approaches in legal reasoning, highlighting their limitations in providing justifications, and proposes integrating expert knowledge to balance scalability and explanation.
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