  Multi-label charge prediction is a task to predict the corresponding
accusations for legal cases, and recently becomes a hot topic. However, current
studies use rough methods to deal with the label number. These methods manually
set parameters to select label numbers, which has an effect in final prediction
quality. We propose an external knowledge enhanced multi-label charge
prediction approach that has two phases. One is charge label prediction phase
with external knowledge from law provisions, the other one is number learning
phase with a number learning network (NLN) designed. Our approach enhanced by
external knowledge can automatically adjust the threshold to get label number
of law cases. It combines the output probabilities of samples and their
corresponding label numbers to get final prediction results. In experiments,
our approach is connected to some state of-the art deep learning models. By
testing on the biggest published Chinese law dataset, we find that our approach
has improvements on these models. We future conduct experiments on multi-label
samples from the dataset. In items of macro-F1, the improvement of baselines
with our approach is 3%-5%; In items of micro-F1, the significant improvement
of our approach is 5%-15%. The experiment results show the effectiveness our
approach for multi-label charge prediction.
