This paper presents the first dataset for Japanese Legal Judgment Prediction (LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort prediction and its rationale extraction. The rationale extraction task identifies the court's accepting arguments from alleged arguments by plaintiffs and defendants, which is a novel task in the field. JTD is constructed based on annotated 3,477 Japanese Civil Code judgments by 41 legal experts, resulting in 7,978 instances with 59,697 of their alleged arguments from the involved parties. Our baseline experiments show the feasibility of the proposed two tasks, and our error analysis by legal experts identifies sources of errors and suggests future directions of the LJP research.
@article{arxiv.2312.00480,
title = {Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction},
author = {Hiroaki Yamada and Takenobu Tokunaga and Ryutaro Ohara and Akira Tokutsu and Keisuke Takeshita and Mihoko Sumida},
journal= {arXiv preprint arXiv:2312.00480},
year = {2025}
}
Comments
14 pages, 5 figures. This is the final preprint version. Accepted and published at Artificial Intelligence and Law (2024)