Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction
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arXiv
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| Format: | Preprint |
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2023
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| author | Yamada, Hiroaki Tokunaga, Takenobu Ohara, Ryutaro Tokutsu, Akira Takeshita, Keisuke Sumida, Mihoko |
| author_facet | Yamada, Hiroaki Tokunaga, Takenobu Ohara, Ryutaro Tokutsu, Akira Takeshita, Keisuke Sumida, Mihoko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_00480 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction Yamada, Hiroaki Tokunaga, Takenobu Ohara, Ryutaro Tokutsu, Akira Takeshita, Keisuke Sumida, Mihoko Computation and Language Artificial Intelligence 68T50 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. |
| title | Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction |
| topic | Computation and Language Artificial Intelligence 68T50 |
| url | https://arxiv.org/abs/2312.00480 |