Recent advances in neural networks enable us to jointly model charge prediction and relevant article extraction in a unified framework, where the latent correspondence from the fact description about a case to its related law articles and further to its charges can be explicitly addressed by a two-stack attention mechanism.
we use a sentence-level and a documentlevel Bi-directional Gated Recurrent Units (Bi-GRU) (Bahdanau et al., 2015) with a stack of factside attention components to model the correlations among words and sentences, in order to capture the whole story as well as important details of the case.