Legal judgment prediction via legal knowledge extraction and fusion

Legal judgment prediction (LJP) task aims to predict relevant law articles, charges and terms of penalty through the textual fact description of legal cases. However, existing methods face the core challenge is external legal knowledge under-exploitation, which hinders the performance of LJP. To overcome this limitation, a legal knowledge enhanced prompt learning based approach (LKEPL) is proposed. Our method first design the legal knowledge extraction which utilizes the model trained on our labeled legal dataset to identify legal entity relation knowledge and obtains law articles knowledge by the attention mechanism. Afterwards, our method employs legal knowledge projector to translate legal knowledge vectors into the transformer space. Then our method incorporates two kinds of knowledge by concatenating those vectors before the key and value matrix of each multi-head attention of BERT. Finally, our method designs the LJP task-specific prompt learning method. Experimental results on the CAIL2018 dataset demonstrate that our method outperforms existing strong baseline models. Furthermore, our method offers interpretability and shows effectiveness under few-shot scenarios.