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From Graph to Word Bag: Introducing Domain Knowledge to Confusing Charge Prediction (2403.04369v3)

Published 7 Mar 2024 in cs.AI and cs.CL

Abstract: Confusing charge prediction is a challenging task in legal AI, which involves predicting confusing charges based on fact descriptions. While existing charge prediction methods have shown impressive performance, they face significant challenges when dealing with confusing charges, such as Snatch and Robbery. In the legal domain, constituent elements play a pivotal role in distinguishing confusing charges. Constituent elements are fundamental behaviors underlying criminal punishment and have subtle distinctions among charges. In this paper, we introduce a novel From Graph to Word Bag (FWGB) approach, which introduces domain knowledge regarding constituent elements to guide the model in making judgments on confusing charges, much like a judge's reasoning process. Specifically, we first construct a legal knowledge graph containing constituent elements to help select keywords for each charge, forming a word bag. Subsequently, to guide the model's attention towards the differentiating information for each charge within the context, we expand the attention mechanism and introduce a new loss function with attention supervision through words in the word bag. We construct the confusing charges dataset from real-world judicial documents. Experiments demonstrate the effectiveness of our method, especially in maintaining exceptional performance in imbalanced label distributions.

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References (38)
  1. Do charge prediction models learn legal theory?
  2. A comparative study of summarization algorithms applied to legal case judgments. In Advances in Information Retrieval, pages 413–428, Cham. Springer International Publishing.
  3. Learning heterogeneous graph embedding for chinese legal document similarity. Knowledge-Based Systems, 250:109046.
  4. Particle swarm optimization based semi-supervised learning on chinese text categorization. 2012 IEEE Congress on Evolutionary Computation, CEC 2012.
  5. A survey on legal judgment prediction: Datasets, metrics, models and challenges. Preprint at https://arxiv.org/abs/2204.04859.
  6. Revisiting pre-trained models for chinese natural language processing. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 284–298, . Association for Computational Linguistics.
  7. Bert: Pre-training of deep bidirectional transformers for language understanding.
  8. Qian Dong and Shuzi Niu. 2021. Legal judgment prediction via relational learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 983–992.
  9. Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
  10. Ben Hachey and Claire Grover. 2006. Extractive summarisation of legal texts. Artificial Intelligence and Law, 14(4):305–345.
  11. Few-shot charge prediction with discriminative legal attributes. In Emily M. Bender, Leon Derczynski, and Pierre Isabelle, editors, Proceedings of the 27th International Conference on Computational Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, pages 487–498. Association for Computational Linguistics, .
  12. Studies on earthquake precursors in china: A review for recent 50 years. Geodesy and Geodynamics, 8(1):1–12.
  13. An end-to-end joint model for evidence information extraction from court record document. Information Processing and Management, 57(6):102305.
  14. Rie Johnson and Tong Zhang. 2017. Deep pyramid convolutional neural networks for text categorization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 562–570.
  15. Creating auxiliary representations from charge definitions for criminal charge prediction. CoRR, abs/1911.05202.
  16. Recurrent convolutional neural networks for text classification. In Proceedings of the AAAI conference on artificial intelligence, volume 29.
  17. Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction. In International Journal of Computational Linguistics & Chinese Language Processing, Volume 17, Number 4, December 2012-Special Issue on Selected Papers from ROCLING XXIV.
  18. Gang Liu and Jiabao Guo. 2019. Bidirectional lstm with attention mechanism and convolutional layer for text classification. Neurocomputing, 337:325–338.
  19. ML-LJP: multi-law aware legal judgment prediction. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, pages 1023–1034. ACM.
  20. Learning to predict charges for criminal cases with legal basis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2727–2736, Copenhagen, Denmark. Association for Computational Linguistics.
  21. Improving legal judgment prediction through reinforced criminal element extraction. Information Processing and Management, 59(1):102780.
  22. Raquel Mochales and Marie-Francine Moens. 2009. Argumentation mining: The detection, classification and structure of arguments in text. Proceedings of the International Conference on Artificial Intelligence and Law, pages 98–107.
  23. Radim Řehůřek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pages 45–50, Valletta, Malta. ELRA. http://is.muni.cz/publication/884893/en.
  24. Ryosuke Taniguchi and Yoshinobu Kano. 2017. Legal yes/no question answering system using case-role analysis. In New Frontiers in Artificial Intelligence, pages 284–298, Cham. Springer International Publishing.
  25. Why fairness cannot be automated: Bridging the gap between eu non-discrimination law and ai. Computer Law and Security Review, 41:105567.
  26. Hierarchical matching network for crime classification. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, July 21-25, 2019, pages 325–334. ACM.
  27. Community preserving network embedding. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI’17, page 203–209. AAAI Press.
  28. Precedent-enhanced legal judgment prediction with LLM and domain-model collaboration. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 12060–12075, Singapore. Association for Computational Linguistics.
  29. Lawformer: A pre-trained language model for chinese legal long documents. AI Open, 2:79–84.
  30. Cail2019-scm: A dataset of similar case matching in legal domain.
  31. Data-driven learning for data rights, data pricing, and privacy computing. Engineering, 25:66–76.
  32. Distinguish confusing law articles for legal judgment prediction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3086–3095, Online. Association for Computational Linguistics.
  33. 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, Volume 1 (Long Papers), pages 1854–1864, New Orleans, Louisiana. Association for Computational Linguistics.
  34. Neurjudge: A circumstance-aware neural framework for legal judgment prediction. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 973–982.
  35. Circumstances enhanced criminal court view generation. In SIGIR, pages 1855–1859.
  36. Legal judgment prediction via topological learning. In Proceedings of the 2018 conference on empirical methods in natural language processing, pages 3540–3549.
  37. How does NLP benefit legal system: A summary of legal artificial intelligence. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5218–5230, Online. Association for Computational Linguistics.
  38. A c-lstm neural network for text classification. arXiv preprint arXiv:1511.08630.
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Authors (7)
  1. Ang Li (472 papers)
  2. Qiangchao Chen (1 paper)
  3. Yiquan Wu (15 papers)
  4. Ming Cai (20 papers)
  5. Xiang Zhou (164 papers)
  6. Fei Wu (317 papers)
  7. Kun Kuang (114 papers)
Citations (1)
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