Abstract
Judgment prediction is a critical technique in legal field. Judges usually scan both of the fact descriptions and articles repeatedly to select valuables information for a correct match (i.e., determine the correct articles for a given fact description). Previous works only analyze semantics to the corresponding articles, while the repeated semantic interactions between fact descriptions and articles are ignored, thus the performance may be limited. In this paper, we propose a novel Recurrent Attention Network (RAN for short) to address this issue. Specifically, RAN utilizes a LSTM to obtain both fact description and article representations, then a recurrent process is designed to model the iterative interactions between fact descriptions and articles to make a correct match. Experimental results on real-world datasets demonstrate that our proposed model achieves significant improvements over the state-of-the-art methods.
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Acknowledgement
This research work was supported by the National Natural Science Foundation of China under Grant No. 61802029, and the fundamental Research for the Central Universities under Grant No. 500419741. We would like to thank the anonymous reviewers for their valuable comments.
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Yang, Z., Wang, P., Zhang, L., Shou, L., Xu, W. (2019). A Recurrent Attention Network for Judgment Prediction. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_21
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DOI: https://doi.org/10.1007/978-3-030-30490-4_21
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