Japanese Tort-case Dataset for Rationale-supported Legal Judgment
  Prediction
Hiroaki Yamada Takenobu Tokunaga Ryutaro Ohara Akira Tokutsu Keisuke Takeshita Mihoko Sumida
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.