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.

翻译：长期以来，逻辑编程在法律推理领域备受推崇，已有多种方法依赖于以逻辑编程术语显式表示法律规则。在这篇立场论文中，我们聚焦于基于逻辑编程的PROLEG框架，用于形式化并推理日本预设终极事实理论。具体而言，我们探讨了利用深度学习技术改进基于PROLEG的法律推理所面临的挑战与机遇，识别出四种不同方案，涵盖从利用深度学习增强事实提取到对文本法律描述进行推理的端到端解决方案。我们评估了每种方案的优劣，同时考虑其技术可行性、可解释性以及与法律从业者和决策者需求的契合度。我们相信，本分析可作为开发者为法律领域构建高效决策支持系统的指南，同时促进对神经符号方法在法律应用中面临的挑战与潜在进展的深入理解。