Abstract
Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.
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References
Chen, H., Cai, D., Dai, W., Dai, Z., Ding, Y.: Charge-based prison term prediction with deep gating network. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 6361–6366 (2019)
Hu, Z., Li, X., Tu, C., Liu, Z., Sun, M.: Few-shot charge prediction with discriminative legal attributes. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 487–498 (2018)
Katz, D.M., Bommarito Ii, M.J., Blackman, J.: Predicting the behavior of the supreme court of the united states: A general approach. Plos One 12(4) (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746–1751 (2014)
Li, Z., Sun, M.: Punctuation as implicit annotations for Chinese word segmentation (2009)
Lin, W.C., Kuo, T.T., Chang, T.J., Yen, C.A., Chen, C.J., Lin, S.D.: Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction in Chinese. In: Proceedings of the 24th Conference on Computational Linguistics and Speech Processing (ROCLING 2012), pp. 140–141 (2012)
Liu, C.L., Hsieh, C.D.: Exploring phrase-based classification of judicial documents for criminal charges in Chinese. In: Foundations of Intelligent Systems, pp. 681–690 (2006)
Liu, Y., Chen, Y., Ho, W.: Predicting associated statutes for legal problems. Inf. Process. Manag. 51(1), 194–211 (2015)
Liu, Z., Zhang, M., Zhen, R., Gong, Z., Yu, N., Fu, G.: Multi-task learning model for legal judgment predictions with charge keywords. J. Tsinghua Univ. (Sci. Technol.) 59(7), 497 (2019)
Luo, B., Feng, Y., Xu, J., Zhang, X., Zhao, D.: Learning to predict charges for criminal cases with legal basis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2727–2736 (2017)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Computer Science (2013)
Wang, W., Chen, Y., Cai, H., Zeng, Y., Yang, H.: Judicial document intellectual processing using hybrid deep neural networks. J. Tsinghua Univ. (Sci. Technol.) 59(7), 505 (2019)
Xiao, C., et al.: CAIL2018: a large-scale legal dataset for judgment prediction. CoRR abs/1807.02478 (2018), http://arxiv.org/abs/1807.02478
Xu, N., Wang, P., Chen, L., Pan, L., Wang, X., Zhao, J.: Distinguish confusing law articles for legal judgment prediction. CoRR abs/2004.02557 (2020). https://arxiv.org/abs/2004.02557
Yang, W., Jia, W., Zhou, X., Luo, Y.: Legal judgment prediction via multi-perspective bi-feedback network. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 4085–4091 (2019)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Ye, H., Jiang, X., Luo, Z., Chao, W.: Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1854–1864 (2018)
Zhong, H., Guo, Z., Tu, C., Xiao, C., Liu, Z., Sun, M.: Legal judgment prediction via topological learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3540–3549 (2018)
Zhong, H., Wang, Y., Tu, C., Zhang, T., Liu, Z., Sun, M.: Iteratively questioning and answering for interpretable legal judgment prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 1250–1257 (2020)
Acknowledgements
This work is supported by National Natural Science Foundation of China (No. 61976062), the Science and Technology Program of Guangzhou (No. 201904010303) and Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (No. pdjh2020a0197).
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Xu, Z., Li, X., Li, Y., Wang, Z., Fanxu, Y., Lai, X. (2020). Multi-task Legal Judgement Prediction Combining a Subtask of the Seriousness of Charges. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_30
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DOI: https://doi.org/10.1007/978-3-030-63031-7_30
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