“Legal judgment prediction (LJP) is a basic task in legal artificial intelligence. It consists ofthree subtasks, which are relevant law article prediction, charge prediction and term of penaltyprediction, and gives the judgment results to assist the work of judges. In recent years, many deeplearning methods have emerged to improve the performance of the legal judgment prediction task. The previous methods mainly improve the performance by integrating law articles and the factdescription of a legal case. However, they rarely consider that the judges usually look up historicalcases before making a judgment in the actual scenario. To simulate this scenario, we propose ahistorical case retrieval framework for the legal judgment prediction task. Specifically, we selectsome historical cases which include all categories from the training dataset. Then, we retrieve themost similar Top-k historical cases of the current legal case and use the vector representation ofthese Top-k historical cases to help predict the judgment results. On two real-world legal datasets,our model achieves better results than several state-of-the-art baseline models.”

Charge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction on high-frequency charges has achieved promising results but that on few-shot chargesis still challenging. In this work we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically we extract coarse- and fine-grained features to enhance the model’s capability on representation based on which the few-shot chargescan be better distinguished. Furthermore we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides our method can work wellon tiny datasets and has better efficiency in the training which provides better applicability in realscenarios.