Legal Elements Extraction via Label Recross Attention and Contrastive Learning
2023 IEEE 6th International Conference on Big Data and Artificial Intelligence (BDAI)(2023)
摘要
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.
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关键词
Legal elements extraction,low-frequent legal elements,multilabel text classification,recross attention matrix,contrastive learning
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