Judgment Prediction via Injecting Legal Knowledge into Neural Networks

Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which aims to predict a law case's judgment based on a given text describing the facts of the law case. Most of previous works treat LJP as a text classification task and generally adopt deep neural networks (DNNs) based methods to solve it. However, existing DNNs based models are data thirsty and hard to explain which legal knowledge is based on to make such a prediction. Thus, injecting legal knowledge into neural networks to interpret the model and improve performance remains a significant problem. In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. The use of logic rules enhances neural networks with direct logical reasoning capabilities and makes the model more interpretable. We take private loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analyses conducted on the collected dataset.