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.

翻译：本文首次提出了面向日本法律判决预测（LJP）的数据集——日本侵权案件数据集（JTD），该数据集包含两个任务：侵权预测及其理由提取。理由提取任务旨在从原告和被告的主张中识别法院采信的法律论证，这是该领域的一项新颖任务。JTD基于41位法律专家对3477份日本民法典判决的标注构建而成，最终获得7978个实例及涉及的59697项当事方主张。我们的基线实验证明了所提两个任务的可行性，法律专家的错误分析确定了错误来源，并为LJP研究的未来方向提供了建议。