  Charge prediction, determining charges for criminal cases by analyzing the
textual fact descriptions, is a promising technology in legal assistant
systems. In practice, the fact descriptions could exhibit a significant
intra-class variation due to factors like non-normative use of language, 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 definitions from criminal law to alleviate this issue. The key
idea is that the expressions in a fact description should have corresponding
formal terms in charge 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. The generated auxiliary representations are
created through the interaction of fact description with the relevant charge
definitions and terms in those definitions by integrated sentence- and
word-level attention scheme. Experimental results on two datasets show that our
model achieves significant improvement than baselines, especially for classes
with few samples.
