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BERT-Based Ensemble Model for Statute Law Retrieval and Legal Information Entailment

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New Frontiers in Artificial Intelligence (JSAI-isAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12758))

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Abstract

The Competition on legal information extraction/entailment (COLIEE) is an international information processing and retrieval competition. As an aid to future participants as well as question designers, this article describes how to connect legal questions taken from past Japanese bar exams to relevant statutes (articles of the Japanese Civil Code, Task 3) and how to construct a Yes/No question answering system for legal queries (Task 4) incorporating background materials on Japanese law. We restructured the given data to a dataset which contains all possible combinations of queries and articles as continuous strings as our samples. In this way, the difficult pairing task has been turned into a simpler classification task and samples for training became sufficient in number. Next, we used three BERT-based models to solve binary questions in order to achieve stable performance. As a result, the model achieved an F2-score of 0.6587 in Task 3 (ranked 1st) and an accuracy of 0.6161 in Task 4.

All authors contributed equally to this work.

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Acknowledgments

This work was financially supported by: Hsuan-Lei Shao, “From Knowledge Genealogy to Knowledge Map-China Studies in Big Data and Machine Learning” (MOST 107-2410-H-003 -058 -MY3), Sieh-Chuen Huang, Center for Research in Econometric Theory and Applications (Grant no. NTU-110L900203) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) and Ministry of Science and Technology (MOST 109-2634-F-002-045) in Taiwan.

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Authors and Affiliations

  1. Department of East Asian Studies, National Taiwan Normal University, Taipei, Taiwan

    Hsuan-Lei Shao

  2. Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan

    Yi-Chia Chen

  3. College of Law, National Taiwan University, Taipei, Taiwan

    Sieh-Chuen Huang

  4. Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan

    Sieh-Chuen Huang

Authors
  1. Hsuan-Lei Shao
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  2. Yi-Chia Chen
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  3. Sieh-Chuen Huang
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Corresponding author

Correspondence to Sieh-Chuen Huang .

Editor information

Editors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Naoaki Okazaki

  2. Kansai Uversity, Osaka, Japan

    Katsutoshi Yada

  3. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  4. Keio University, Tokyo, Japan

    Koji Mineshima

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Shao, HL., Chen, YC., Huang, SC. (2021). BERT-Based Ensemble Model for Statute Law Retrieval and Legal Information Entailment. 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_15

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  • DOI: https://doi.org/10.1007/978-3-030-79942-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79941-0

  • Online ISBN: 978-3-030-79942-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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