BBAE: A Method for Few-Shot Charge Prediction with Data Augmentation and Neural Network

Charge prediction aims to predict charges from the case descriptions and plays a significant role in legal assistance systems. When we use deep learning methods, prediction on high-frequency charges has achieved promising results, but prediction on few-shot charges is still a challenge. To address this issue, a few-shot charge predictionmethod with data augmentation and neural network is proposed, named BBAE (BERT-BiGRU-Attention based on easy data augmentation techniques), which can be divided into three layers: data augmentation layer, encoder layer, and output layer. Specifically, the data augmentation layer takes the case description as input and uses EDA (easy data augmentation techniques) to generate synthetic samples biased to few-shot charges based on charge categories. The encoder layer employs the BERT-BiGRU-Attention model to fully extract text features, while the output layer predicts the charge on the basis of text features. Experiments on three public datasets of Chinese criminal cases demonstrate that our method achieves more effective improvements over other baselines. BBAE outperforms state-of-the-art methods by 4.6% and 9.3% under Macro F1 in low-frequency and medium-frequency charges, which indicates that our method is effective in few-shot charge prediction.