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Information Extraction/Entailment of Common Law and Civil Code

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New Frontiers in Artificial Intelligence (JSAI-isAI 2020)

Abstract

With the recent advancements in machine learning models, we have seen improvements in Natural Language Inference (NLI) tasks, but legal entailment has been challenging, particularly for supervised approaches. In this paper, we evaluate different approaches on handling entailment tasks for small domain-specific data sets provided in the Competition on Legal Information Extraction/Entailment (COLIEE). This year COLIEE had four tasks, which focused on legal information processing and finding textual entailment on legal data. We participated in all the four tasks this year, and evaluated different kinds of approaches, including classification, ranking, and transfer learning approaches against the entailment tasks. In some of the tasks, we achieved competitive results when compared to simpler rule-based approaches, which so far have dominated the competition for the last six years.

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Notes

  1. 1.

    Extracted pages to from https://ja.wikibooks.org/wiki/

  2. 2.

    The articles where translated from Japanese to English using the Google translation API.

References

  1. Bender, E.M., Koller, A.: Climbing towards NLU: on meaning, form, and understanding in the age of data. In: Proceedings of ACL 2020 (2020). https://doi.org/10.18653/v1/2020.acl-main.463

  2. Branting, K., et al.: Semi-supervised methods for explainable legal prediction. In: Proceedings of ICAIL 2019 (2019). https://doi.org/10.1145/3322640.3326723

  3. Goldberg, Y., Levy, O.: word2vec explained: deriving Mikolov et al’.s negative-sampling word-embedding method (2014). http://arxiv.org/abs/1402.3722

  4. Hudzina, J., Vacek, T., Madan, K., Custis, T., Schilder, F.: Statutory entailment using similarity features and decomposable attention models. In: Proceedings of COLIEE 2019 (2019)

    Google Scholar 

  5. Kim, M., Rabelo, J., Goebel, R.: Statute law information retrieval and entailment. In: Proceedings of ICAIL 2019 (2019). https://doi.org/10.1145/3322640.3326742

  6. Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach (2019). https://arxiv.org/abs/1907.11692

  7. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP (2014)

    Google Scholar 

  8. Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1–67 (2020). http://jmlr.org/papers/v21/20-074.html

    MathSciNet  MATH  Google Scholar 

  9. Son, N.T., Phan, V.A., Minh, N.L.: Recognizing entailments in legal texts using sentence encoding-based and decomposable attention models. In: Proceedings of COLIEE 2017 (2017). http://www.easychair.org/publications/paper/347231

  10. Trivedi, H., Kwon, H., Khot, T., Sabharwal, A., Balasubramanian, N.: Repurposing entailment for multi-hop question answering tasks. In: Proceedings of NAACL 2019 (2019). https://doi.org/10.18653/v1/N19-1302

  11. Wang, A., et al.: SuperGLUE: a stickier benchmark for general-purpose language understanding systems. arXiv preprint arXiv:1905.00537 (2019)

  12. Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of NAACL-HLT 2018 (2018). http://aclweb.org/anthology/N18-1101

  13. Wolf, T., et al.: HuggingFace’s transformers: state-of-the-art natural language processing. ArXiv abs/1910.03771 (2019)

    Google Scholar 

  14. Yoshioka, M., Kano, Y., Kiyota, N., Satoh, K.: Overview of Japanese statute law retrieval and entailment task at COLIEE-2018 (2018). https://sites.ualberta.ca/~rabelo/COLIEE2019/COLIEE2018_SL_summary.pdf

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Correspondence to John Hudzina .

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Hudzina, J. et al. (2021). Information Extraction/Entailment of Common Law and Civil Code. In: Okazaki, N., Yada, K., Satoh, K., Mineshima, K. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2020. Lecture Notes in Computer Science(), vol 12758. Springer, Cham. https://doi.org/10.1007/978-3-030-79942-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-79942-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79941-0

  • Online ISBN: 978-3-030-79942-7

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