Mulan: a multiple residual article-wise attention network for legal judgment prediction

Junyi Chen, Lan Du, Ming Liu, Xiabing Zhou

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

Legal judgment prediction (LJP) is used to predict judgment results based on the description of individual legal cases. In order to be more suitable for actual application scenarios in which the case has cited multiple articles and has multiple charges, we formulate legal judgment prediction as a multiple label learning problem and present a deep learning model that can effectively encode the content of each legal case via a multi-residual convolution neural network and the semantics of law articles via an article encoder. An article-wise attention mechanism is proposed to fuse the two types of encoded information. Experimental results derived on the CAIL2018 datasets show that our model provides a significant performance improvement over the existing neural models in predicting relevant law articles and charges.

Original languageEnglish
Article number81
Number of pages15
JournalACM Transactions on Asian and Low-Resource Language Information Processing
Volume21
Issue number4
DOIs
Publication statusPublished - Jul 2022

Keywords

  • Legal judgment prediction
  • Neural networks

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