Abstract
The charge prediction task for multi-defendant cases is to determine appropriate charges for a specific defendant according to its name and its fact description. This task is not trivial since it is hard to recognize fact descriptions for different defendants. Therefore, we propose a multi-scale attention model for this problem. We employ local attention, which is highly related to the position of the specific defendant’s name appear in the fact description, to restrict our model to the description for a specific defendant and employ global attention, which is calculated by a charge prediction model for single-defendant cases, to supplement the model with global information of the case. We collect about 160,000 indictments for experiments. After data preprocessing, we choose the two most common charge pairs which are Theft with Concealment of Crime-related Income, and Open Casinos with Gamble for experiments. Experimental results show the effectiveness of our model, the multi-scale attention model does benefit from the global information from the complete case compared to the local attention model.
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References
Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., Lampos, V.: Predicting judicial decisions of the european court of human rights: a natural language processing perspective. PeerJ Comput. Sci. 2, e93 (2016)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
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)
Jiang, X., Ye, H., Luo, Z., Chao, W., Ma, W.: Interpretable rationale augmented charge prediction system. In: Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pp. 146–151 (2018)
Keown, R.: Mathematical models for legal prediction. Computer/lj 2, 829 (1980)
Kort, F.: Predicting supreme court decisions mathematically: a quantitative analysis of the “right to counsel” cases. Am. Polit. Sci. Rev. 51(1), 1–12 (1957)
Liu, C.-L., Hsieh, C.-D.: Exploring phrase-based classification of judicial documents for criminal charges in Chinese. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds.) ISMIS 2006. LNCS (LNAI), vol. 4203, pp. 681–690. Springer, Heidelberg (2006). https://doi.org/10.1007/11875604_75
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Luo, B., Feng, Y., Xu, J., Zhang, X., Zhao, D.: Learning to predict charges for criminal cases with legal basis. arXiv preprint arXiv:1707.09168 (2017)
Nagel, S.S.: Applying correlation analysis to case prediction. Tex. L. Rev. 42, 1006 (1963)
Xiao, C., et al.: Cail 2018: a large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478 (2018)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 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. arXiv preprint arXiv:1802.08504 (2018)
Zhong, H., Zhipeng, G., 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)
Acknowledgement
This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFC0381402 and the project of Guangdong Provincial Joint Laboratory of Natural Language Processing and Machine Learning.
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Pan, S., Lu, T., Gu, N., Zhang, H., Xu, C. (2019). Charge Prediction for Multi-defendant Cases with Multi-scale Attention. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_59
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DOI: https://doi.org/10.1007/978-981-15-1377-0_59
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