Judgment Prediction via Injecting Legal Knowledge into Neural Networks
AbstractLegal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods to solve it. However, existing DNNs based work is data-hungry 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 explicit logical reason capabilities and makes the model more interpretable. We take the civil loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analysis conducted on the collected dataset.
How to Cite
Gan, L., Kuang, K., Yang, Y., & Wu, F. (2021). Judgment Prediction via Injecting Legal Knowledge into Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12866-12874. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17522
AAAI Technical Track on Speech and Natural Language Processing I