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The charge prediction task aims to predict appropriate charges for a given legal case automatically, which still confronts some challenging problems such as performance improvement and confusing charges issue. In this paper, inspired by the impressive success of deep neural networks in legal intelligence field, we present an end-to-end framework named law article deduplication attention neural network, ADAN, to address these problems. The incorporation of hierarchical sequence encoder and attention mechanism is employed to learn better semantic representations of fact description texts. To distinguish confusing charges, we use the relevant law articles of a given case as auxiliary information, and propose a novel difference aggregation mechanism among similar law articles for extracting effective distinguishable features. The experimental results on real-world datasets show that the performance of our proposed model is significantly better than existing methods on all evaluation metrics.

ADAN: An Intelligent Approach Based on Attentive Neural Network and Relevant Law Articles for Charge Prediction