Abstract
Automatic law article prediction aims to determine appropriate laws for a case by analyzing its corresponding fact description. This research constitutes a relatively new area which has emerged from recommended algorithm. Therefore, the task is still a challenge due to the highly imbalanced long-tail data distribution and lack of discrimination in the feature representation. To deal with these challenges, we proposed a codex enhanced multi-task framework, which consists of two modules. The first one is a codex learning module that estimates the broad codex attributes related to the case fact for alleviating the long-tail issue. The other one is a Bidirectional Text Convolutional Neural Network, which predicts the law articles by considering both local and global information of the facts. These two modules are learning simultaneously through a multi-task loss function. To evaluate the performance of the method proposed in this paper, we construct a new law article prediction data by collecting the judgment documents from the China Judgement online. Experimental results on the dataset demonstrate the effectiveness of our proposed method and can outperform other comparison methods.
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Acknowlegdement
This work is supported by the National Key Research and Development Program of China (No. 2018YFC0831402), and the Nature Science Foundation of China (No. 62076210 & 61806172).
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Liu, B., Luo, Z., Lin, D., Cao, D. (2021). Law Article Prediction via a Codex Enhanced Multi-task Learning Framework. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_14
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