AI 中文总结
针对金融 ADR 案例，提出统一数据集和建模框架，引入功能标记方案表示争议结构，构建多任务模型联合进行争议分类与和解预测，实验显示该方法能提升预测性能，发现争议结构在 ADR 领域部分共享。
AI 中文摘要
本文为从多个日本 ADR 组织收集的金融替代性纠纷解决（ADR）案例提供了一个统一的数据集和建模框架。每个案例由申诉人和被申诉人的成对索赔以及二元和解结果组成。我们引入一种功能标记方案来表示争议结构，并提出一个联合执行争议分类和和解预测的多任务模型。实验结果表明，纳入争议结构可提高预测性能，并且大语言模型在多个领域取得了相当或更好的性能。这些发现表明争议结构在 ADR 领域中部分共享。
英文摘要
This paper presents a unified dataset and modeling framework for financial alternative dispute resolution (ADR) cases collected from multiple Japanese ADR organizations. Each case consists of paired claims from the complainant and the respondent with a binary settlement outcome. We introduce a functional tagging scheme to represent dispute structures and propose a multi-task model that jointly performs dispute classification and settlement prediction. Experimental results show that incorporating dispute structure improves prediction performance, and large language models achieve comparable or superior performance in several domains. These findings suggest that dispute structures are partially shared across ADR domains.
Comments4th International Conference on Computational and Data Sciences in Economics and Finance (CDEF 2026)
