Logic programming has long being advocated for legal reasoning, and several
approaches have been put forward relying upon explicit representation of the
law in logic programming terms. In this position paper we focus on the PROLEG
logic-programming-based framework for formalizing and reasoning with Japanese
presupposed ultimate fact theory. Specifically, we examine challenges and
opportunities in leveraging deep learning techniques for improving legal
reasoning using PROLEG identifying four distinct options ranging from enhancing
fact extraction using deep learning to end-to-end solutions for reasoning with
textual legal descriptions. We assess advantages and limitations of each
option, considering their technical feasibility, interpretability, and
alignment with the needs of legal practitioners and decision-makers. We believe
that our analysis can serve as a guideline for developers aiming to build
effective decision-support systems for the legal domain, while fostering a
deeper understanding of challenges and potential advancements by neuro-symbolic
approaches in legal applications.Comment: Workshop on Logic Programming and Legal Reasoning, @ICLP 202