  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
