TY - JOUR AU - Gan, Leilei AU - Kuang, Kun AU - Yang, Yi AU - Wu, Fei PY - 2021/05/18 Y2 - 2024/03/28 TI - Judgment Prediction via Injecting Legal Knowledge into Neural Networks JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 14 SE - AAAI Technical Track on Speech and Natural Language Processing I DO - 10.1609/aaai.v35i14.17522 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17522 SP - 12866-12874 AB - Legal 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. ER -