Abstract
Charge prediction aims to predict the corresponding charges for a specific case. In civil law system, human judges will match the facts with relevant laws, and the final judgments are usually made in accordance with relevant law articles. Existing works either ignore this feature or simply model the relationship using multi-task learning, but neither make full use of relevant articles to assist the charge prediction task. To address this issue, we propose an attentional neural network, LegalAtt, which uses relevant articles to improve the performance and interpretability of charge prediction task. More specifically, our model works in a bidirectional approach: First, it uses the fact description to extract relevant articles; In return, the selected relevant articles assist to locate key information from the fact description, which helps improve the performance of charge prediction. Experimental results show that our model achieves the best performance on the real-world dataset compared with other state-of-the-art baselines. Our code is available at https://github.com/nlp208/legal_attention.
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Acknowledgements
This work is supported by the National Social Science Fund of China (No. 18ZDA315) and Big Data Application on lmproving Government Governance Capabilities National Engineering Laboratory Open Fund Project.
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Bao, Q., Zan, H., Gong, P., Chen, J., Xiao, Y. (2019). Charge Prediction with Legal Attention. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_35
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DOI: https://doi.org/10.1007/978-3-030-32233-5_35
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