Legal Elements Extraction via Label Recross Attention and Contrastive Learning

Automatic extraction of legal elements (LEs) from legal documents is essential for constructing a smart court of law. While this task has met with certain success, how to distinguish similar LEs that appear infrequently in the training set remains a challenge, particularly when the number of training documents is limited and the cost of labeling additional documents is high. To overcome this obstacle, we present L-RACL, a neural-net model with label recross attention and contrastive loss to distinguish similar LE labels and capture correlations between LE labels. Extensive experiments show that L-RACL outperforms existing methods. Finally, we finetune ChatGPT to perform LE extraction and show that L-RACL surpasses it.