🤖 AI Summary
This study addresses the lack of a unified data and modeling framework in financial alternative dispute resolution (ADR), which hinders accurate prediction of settlement outcomes. To overcome this limitation, the authors integrate data from multiple Japanese ADR institutions and propose a functional-label-based annotation scheme to characterize dispute structures. Building upon this representation, they develop a multi-task learning model that jointly performs dispute classification and settlement prediction. The work introduces, for the first time, a cross-institutional shared representation of dispute structure and demonstrates its partial generalizability across diverse ADR domains. Experimental results show that incorporating structural information significantly enhances settlement prediction performance, and when combined with large language models, the approach achieves or surpasses state-of-the-art results across multiple domains.
📝 Abstract
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.