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arXiv 2306.16632cs.LO
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超越用于法律推理的逻辑编程

Beyond Logic Programming for Legal Reasoning

Ha-Thanh Nguyen, Francesca Toni, Kostas Stathis, Ken Satoh

AI总结:

本文探讨了利用深度学习技术改进基于PROLEG框架的法律推理,提出了四种方案并评估其技术可行性与可解释性,为构建法律决策支持系统提供指导。

摘要:

长期以来,逻辑编程一直被提倡用于法律推理,并且已经提出了几种依赖于以逻辑编程术语对法律进行显式表示的方法。在这篇立场论文中,我们关注用于形式化和推理日本预设最终事实理论的基于逻辑编程的PROLEG框架。具体而言,我们研究了利用深度学习技术通过PROLEG改进法律推理的挑战与机遇,并确定了四种不同的选项,从使用深度学习增强事实提取到使用文本法律描述进行推理的端到端解决方案。我们评估了每个选项的优势和局限性,考虑了它们的技术可行性、可解释性以及与法律从业者和决策者需求的一致性。我们相信,我们的分析可以作为旨在为法律领域构建有效决策支持系统的开发人员的指南,同时促进对神经符号方法在法律应用中挑战和潜在进步的更深入理解。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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