Learning legal text representations via disentangling elements

Yingzhi Miao, Fang Zhou, Martin Pavlovski, Weining Qian

Abstract: Highlights•This work is the first to learn disentangled representations of fact descriptions.•A supervised neural model based on a triplet loss is presented.•The model maximally preserves the information relevant to a specific element.•The learned representations are task-independent.