Learning Dispute Structure for Settlement Prediction in Financial ADR: A Multi-Task and Cross-Institutional Approach
arXiv:2607.17112
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.
4th International Conference on Computational and Data Sciences in Economics and Finance (CDEF 2026)