Few-Shot Domain Adaptation for Charge Prediction on Unprofessional Descriptions

TL;DR

This paper introduces a novel few-shot domain adaptation method, DLCCP, that disentangles content and style representations to improve charge prediction accuracy on non-professional, layperson descriptions, addressing data scarcity and domain discrepancy.

Contribution

The paper proposes DLCCP, a disentangled content-style model for few-shot domain adaptation in charge prediction, and releases the first non-PLLS dataset NCCP for layperson descriptions.

Findings

DLCCP outperforms baseline models on NCCP dataset.

Disentangling content and style improves domain-invariant feature learning.

The NCCP dataset enables research on layperson legal language understanding.

Abstract

Recent works considering professional legal-linguistic style (PLLS) texts have shown promising results on the charge prediction task. However, unprofessional users also show an increasing demand on such a prediction service. There is a clear domain discrepancy between PLLS texts and non-PLLS texts expressed by those laypersons, which degrades the current SOTA models' performance on non-PLLS texts. A key challenge is the scarcity of non-PLLS data for most charge classes. This paper proposes a novel few-shot domain adaptation (FSDA) method named Disentangled Legal Content for Charge Prediction (DLCCP). Compared with existing FSDA works, which solely perform instance-level alignment without considering the negative impact of text style information existing in latent features, DLCCP (1) disentangles the content and style representations for better domain-invariant legal content learning with carefully designed optimization goals for content and style spaces and, (2) employs the constitutive elements knowledge of charges to extract and align element-level and instance-level content representations simultaneously. We contribute the first publicly available non-PLLS dataset named NCCP for developing layperson-friendly charge prediction models. Experiments on NCCP show the superiority of our methods over competitive baselines.