Masked prediction and interdependence network of the law using data from large-scale Japanese court judgments
Masked prediction and interdependence network of the law using data from large-scale Japanese court judgments
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DOI：
10.1007/s10506-022-09336-5
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发表时间：
2022-10
影响因子：
4.1
通讯作者：
Ryoma Kondo;Takahiro Yoshida;Ryohei Hisano
中科院分区：
文献类型：
--
作者：
Ryoma Kondo;Takahiro Yoshida;Ryohei Hisano
法院判决包含了宝贵的信息，说明如何解释成文法和过去的法院判例，以及它们之间的相互依存结构如何在法庭上演变。从学术和工业的角度来看，从大规模的法院判决语料库中挖掘反映无数社会价值观的这种习俗和规范的演变结构是一项重要任务。本文使用1998年至2018年期间从地方法院到最高法院的约11万份法院判决的数据，提出了两项任务，以从法院判决中把握这种结构，并突出主要机器学习模型的优点和缺点。一个是基于掩蔽语言建模的预测任务，将文本信息与法律的代码和过去的法院判例联系起来。另一个是动态链接预测任务，我们预测法律中隐藏的相互依赖结构。我们对主要的机器学习模型进行了定量和定性的比较，以获得未来发展的见解。
Court judgments contain valuable information on how statutory laws and past court precedents are interpreted and how the interdependence structure among them evolves in the courtroom. Data-mining the evolving structure of such customs and norms that reflect myriad social values from a large-scale court judgment corpus is an essential task from both the academic and industrial perspectives. In this paper, using data from approximately 110,000 court judgments from Japan spanning the period 1998–2018 from the district to the supreme court level, we propose two tasks that grasp such a structure from court judgments and highlight the strengths and weaknesses of major machine learning models. One is a prediction task based on masked language modeling that connects textual information to legal codes and past court precedents. Another is a dynamic link prediction task where we predict the hidden interdependence structure in the law. We make quantitative and qualitative comparisons among major machine learning models to obtain insights for future developments.