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.

该研究提出了用于日本法律判决预测的首个数据集，即日本侵权案例数据集（JTD），其中包含二个任务：侵权预测和相关性抽取。JTD 基于 41 名法律专家对 3477 个日本民法判决进行了标注，共获得 7978 个实例和 59697 个涉案方的主张。实验结果表明所提出的两个任务的可行性，并且通过法律专家的错误分析确定了错误来源并提出了未来的研究方向。