  Automatic charge prediction task aims to determine the final charges based on
fact descriptions of criminal cases, which is a vital application of legal
assistant systems. Conventional works usually depend on fact descriptions to
predict charges while ignoring the legal schematic knowledge, which makes it
difficult to distinguish confusing charges. In this paper, we propose a
knowledge-attentive neural network model, which introduces legal schematic
knowledge about charges and exploit the knowledge hierarchical representation
as the discriminative features to differentiate confusing charges. Our model
takes the textual fact description as the input and learns fact representation
through a graph convolutional network. A legal schematic knowledge transformer
is utilized to generate crucial knowledge representations oriented to the legal
schematic knowledge at both the schema and charge levels. We apply a knowledge
matching network for effectively incorporating charge information into the fact
to learn knowledge-aware fact representation. Finally, we use the
knowledge-aware fact representation for charge prediction. We create two
real-world datasets and experimental results show that our proposed model can
outperform other state-of-the-art baselines on accuracy and F1 score,
especially on dealing with confusing charges.
