Learning Dispute Structure for Settlement Prediction in Financial ADR: A Multi-Task and Cross-Institutional Approach
Computational Engineering, Finance, and Science
Summary
The authors collected data from different Japanese financial dispute resolution groups, where each case shows what both sides claimed and whether they settled or not. They created a way to label parts of the disputes to better understand their structure. Using this, they built a model that can predict the type of dispute and if it will settle. Their tests showed that knowing the dispute structure helps predict outcomes better, and big language models did well too. They suggest that some dispute patterns are similar across different types of cases.
Authors
Koutarou Tamura
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.