  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.
