@inproceedings{Nguyen:2023,
author = {Nguyen, H-T and Toni, F and Stathis, K and Satoh, K},
title = {Beyond Logic Programming for Legal Reasoning},
url = {http://hdl.handle.net/10044/1/105042},
year = {2023}
}

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TY  - CPAPER
AB  - Logic programming has long being advocated for legal reasoning, and several approaches have been putforward relying upon explicit representation of the law in logic programming terms. In this positionpaper we focus on the PROLEG logic-programming-based framework for formalizing and reasoningwith Japanese presupposed ultimate fact theory. Specifically, we examine challenges and opportunitiesin leveraging deep learning techniques for improving legal reasoning using PROLEG, identifying fourdistinct options ranging from enhancing fact extraction using deep learning to end-to-end solutionsfor 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 practitionersand decision-makers. We believe that our analysis can serve as a guideline for developers aiming tobuild effective decision-support systems for the legal domain, while fostering a deeper understanding ofchallenges and potential advancements by neuro-symbolic approaches in legal applications.
AU  - Nguyen,H-T
AU  - Toni,F
AU  - Stathis,K
AU  - Satoh,K
PY  - 2023///
TI  - Beyond Logic Programming for Legal Reasoning
UR  - http://hdl.handle.net/10044/1/105042
ER  - 

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