Label definitions augmented interaction model for legal charge prediction

Abstract

Charge prediction, determining charges for cases by analyzing the textual fact descriptions, is a fundamental technology in legal information retrieval systems. In practice, the fact descriptions could exhibit a significant intra-class variation due to factors like non-normative use of language by different users, which makes the prediction task very challenging, especially for charge classes with too few samples to cover the expression variation. In this work, we explore to use the charge (label) definitions to alleviate this issue. The key idea is that the expressions in a fact description should have corresponding formal terms in label definitions, and those terms are shared across classes and could account for the diversity in the fact descriptions. Thus, we propose to create auxiliary fact representations from charge definitions to augment fact descriptions representation. Specifically, we design label definitions augmented interaction model, where fact description interacts with the relevant charge definitions and terms in those definitions by a sentence- and word-level attention scheme, to generated auxiliary representations. Experimental results on two datasets show that our model achieves significant improvement than baselines, especially for dataset with few samples.