Legal Intelligence for E-commerce: Multi-task Learning by Leveraging Multiview Dispute Representation

Abstract

Various e-commerce platforms produce millions of transactions per day with many transaction disputes. This generates the demand for effective and efficient dispute resolutions for e-commerce transactions. This paper proposes a novel research task of Legal Dispute Judgment (LDJ) prediction for e-commerce transactions, which connects two yet isolated domains, e-commerce data mining and legal intelligence. Different from traditional legal intelligence with the focus on textual evidence of the dispute itself, the new research utilizes multiview information such as past behavior information of seller and buyer as well as textual evidence of the current transaction. The multiview dispute representation is integrated into an innovative multi-task learning framework for predicting the legal result. An extensive set of experiments with a large dispute case dataset collected from a world leading e-commerce platform shows that the proposed model can more accurately characterize a dispute case through buyer, seller, and transaction viewpoints for legal judgment prediction against several alternatives.