Elamur Research
☰
home / papers / 2018 / Legal Judgment Prediction via Topological Learning

Legal Judgment Prediction via Topological Learning

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/D18-1390.pdf

Summary

The paper addresses Legal Judgment Prediction (LJP) in civil law systems. Earlier work mostly treated it as a single text classification problem, such as predicting only the charge. In practice, a judgment has several linked parts. A judge first identifies the applicable law articles, then decides the charges they imply, and finally sets the term of penalty and fines. The authors model these ordered dependencies with a Directed Acyclic Graph (DAG), and on this basis they introduce TopJudge, a multi-task framework. It encodes the fact description once and then solves the subtasks in topological order. Each subtask's prediction is conditioned on the outputs of the subtasks it depends on, and the design can accommodate any DAG of subtasks. Experiments on three large real-world collections of Chinese criminal cases covered law articles, charges and term of penalty. TopJudge consistently beat single-task baselines and conventional multi-task models. The work matters because enforcing legal ordering reduces contradictory outputs, for example a charge that does not fit the predicted sentence. It also makes the predictions easier to interpret.

Key points

  • Subtask dependencies are formalized as a DAG. Each subtask is predicted by an RNN cell whose initialization carries information from its prerequisite subtasks, combined with task-specific representations. Ablations show that both components are needed for the gains.
  • On the PKU dataset, TopJudge reaches about 95% accuracy for law articles and charges, with macro-F1 of 68.4 and 70.9. For term of penalty it reaches 57.8 accuracy and 31.8 macro-F1. Removing all dependencies lowers term-of-penalty macro-F1 to 27.6.
  • In case studies, CNN-MTL produced inconsistent outputs, such as an arson charge paired with a 1–2 year term, or confusion between similar charges. TopJudge avoided these by using its law-article predictions.
  • Remaining errors come from class imbalance, since rare outcomes like life imprisonment have few training cases. They also come from fact descriptions that omit sentencing-relevant details, such as whether the defendant is a minor.
  • The code is released at github.com/thunlp/TopJudge.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing · Law

Cited by

136 papers of our library cite it (of the 140673 whose bibliographies we read); 8 of them have a page here, 128 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/d18-1390

@inproceedings{zhong2018legal,
  title = {{Legal Judgment Prediction via Topological Learning}},
  author = {Zhong, Haoxi and Guo, Zhipeng and Tu, Cunchao and Xiao, Chaojun and Liu, Zhiyuan and Sun, Maosong},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1390},
  url = {https://doi.org/10.18653/v1/d18-1390}
}

References

40 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Nikolaos Aletras, Dimitrios Tsarapatsanis, Daniel Preotiuc-Pietro, and Vasileios Lampos. 2016. Predicting judicial decisions of the european court of human rights: A natural language processing perspective. PeerJ Computer Science, 2. find
  2. Isabelle Augenstein, Sebastian Ruder, and Anders Søgaard. 2018. Multi-task learning of pairwise sequence classification tasks over disparate label spaces. In Proceedings of NAACL. find
  3. Baharum Baharudin, Lam Hong Lee, and Khairullah Khan. 2010. A review of machine learning algorithms for text-documents classification. Journal of Advances in Information Technology, 1(1):4–20. find
  4. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of ICML, pages 160–167. find
  5. Li Deng, Geoffrey Hinton, and Brian Kingsbury. 2013. New types of deep neural network learning for speech recognition and related applications: An overview. In Proceedings of ICASSP, pages 8599– 8603. find
  6. Daxiang Dong, Hua Wu, Wei He, Dianhai Yu, and Haifeng Wang. 2015. Multi-task learning for multiple language translation. In Proceedings of ACL, volume 1, pages 1723–1732. find our card
  7. Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of ACL, volume 2, pages 845–850. find
  8. Orhan Firat, Kyunghyun Cho, and Yoshua Bengio. 2016. Multi-way, multilingual neural machine translation with a shared attention mechanism. In Proceedings of NAACL, pages 866–875. find our card
  9. Ross Girshick. 2015. Fast r-cnn. In Proceedings of ICCV, pages 1440–1448. find
  10. Kazuma Hashimoto, Yoshimasa Tsuruoka, Richard Socher, et al. 2017. A joint many-task model: Growing a neural network for multiple nlp tasks. In Proceedings of EMNLP, pages 1923–1933. find our card
  11. Sepp Hochreiter and Jurgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780. ↗
  12. Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018. Few-shot charge prediction with discriminative legal attributes. In Proceedings of COLING. find
  13. Daniel Martin Katz, Michael J Bommarito II, and Josh Blackman. 2017. A general approach for predicting the behavior of the supreme court of the united states. Plos one, 12(4). find
  14. R Keown. 1980. Mathematical models for legal prediction. Computer/LJ, 2:829. find
  15. Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of EMNLP. find our card
  16. Diederik Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of ICLR. find
  17. Fred Kort. 1957. Predicting supreme court decisions mathematically: A quantitative analysis of the ”right to counsel” cases. American Political Science Review, 51(1):1–12. find
  18. Benjamin E Lauderdale and Tom S Clark. 2012. The supreme court’s many median justices. American Political Science Review, 106(4):847–866. find
  19. Wanchen Lin, Tsung Ting Kuo, and Tung Jia Chang. 2012. Exploiting machine learning models for chinese legal documents labeling, case classification, and sentencing prediction. In Processdings of RO-CLING, page 140. find
  20. Chaolin Liu, Cheng Tsung Chang, and Jim How Ho. 2004. Case instance generation and refinement for case-based criminal summary judgments in chinese. Journal of Informationence & Engineering, 20(4):783–800. find
  21. Chaolin Liu and Chwen Dar Hsieh. 2006. Exploring phrase-based classification of judicial documents for criminal charges in chinese. In Proceedings of IS-MIS, pages 681–690. find
  22. Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2016. Recurrent neural network for text classification with multi-task learning. In Proceedings of IJCAI. find
  23. Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-Yi Wang. 2015. Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In Proceedings of NAACL, pages 912–921. find
  24. Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017. Learning to predict charges for criminal cases with legal basis. In Proceedings of EMNLP. find
  25. Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, et al. 2018. Cail2018: A large-scale legal dataset for judgment prediction. arXiv preprint arXiv:1807.02478. ↗ our card
  26. Minh-Thang Luong, Quoc V Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2016. Multi-task sequence to sequence learning. In Proceedings of ICLR. find our card
  27. Yongxin Yang and Timothy Hospedales. 2017. Deep multi-task representation learning: A tensor factorisation approach. In Proceedings of ICLR. find
  28. Jiayuan Mao, Tete Xiao, Yuning Jiang, and Zhimin Cao. 2017. What can help pedestrian detection? In Proceedings of CVPR, pages 3127–3136. find
  29. Hai Ye, Xin Jiang, Zhunchen Luo, and Wenhan Chao. 2018. Interpretable charge predictions for criminal cases: Learning to generate court views from fact descriptions. In Proceedings of NAACL. find
  30. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of NIPS, pages 3111–3119. find
  31. Stuart S Nagel. 1963. Applying correlation analysis to case prediction. Texas Law Review, 42:1006. find
  32. Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2017. Learning what to share between loosely related tasks. arXiv preprint arXiv:1705.08142. ↗
  33. Gerard Salton and Christopher Buckley. 1988. Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5):513– 523. find
  34. Jeffrey A Segal. 1984. Predicting supreme court cases probabilistically: The search and seizure cases, 1962-1981. American Political Science Review, 78(4):891–900. find
  35. Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. JMLR, 15(1):1929–1958. find
  36. Octavia Maria Sulea, Marcos Zampieri, Mihaela Vela, and Josef Van Genabith. 2017. Exploring the use of text classi cation in the legal domain. In Proceedings of ASAIL workshop. find
  37. Maosong Sun, Xinxiong Chen, Kaixu Zhang, Zhipeng Guo, and Zhiyuan Liu. 2016. Thulac: An efficient lexical analyzer for chinese. find
  38. Johan AK Suykens and Joos Vandewalle. 1999. Least squares support vector machine classifiers. Neural processing letters, 9(3):293–300. find
  39. Duyu Tang, Bing Qin, and Ting Liu. 2015. Document modeling with gated recurrent neural network for sentiment classification. In Proceedings of EMNLP, pages 1422–1432. find
  40. S Sidney Ulmer. 1963. Quantitative analysis of judicial processes: Some practical and theoretical applications. Law and Contemporary Problems, 28:164. find