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NOWJ at COLIEE 2023: Multi-task and Ensemble Approaches in Legal Information Processing

Author

Listed:
  • Thi-Hai-Yen Vuong

    (University of Engineering and Technology, VNU)

  • Hai-Long Nguyen

    (University of Engineering and Technology, VNU)

  • Tan-Minh Nguyen

    (University of Engineering and Technology, VNU)

  • Hoang-Trung Nguyen

    (University of Engineering and Technology, VNU)

  • Thai-Binh Nguyen

    (University of Engineering and Technology, VNU)

  • Ha-Thanh Nguyen

    (National Institute of Informatics)

Abstract

This paper presents the NOWJ team’s approach to the COLIEE 2023 Competition, which focuses on advancing legal information processing techniques and applying them to real-world legal scenarios. Our team tackled the four tasks in the competition, which involved legal case retrieval, legal case entailment, statute law retrieval, and legal textual entailment. We employ state-of-the-art machine learning models and innovative approaches, such as BERT, Longformer, BM25-ranking algorithm, and multi-task learning models. Our participation in the COLIEE 2023 has provided useful insights including the importance of the pre-processing and feature engineering, effectiveness of the multi-task models in combining different legal tasks to improve model’s performance. Although our team did not achieve state-of-the-art results, our findings identify areas for further research and improvements in legal information processing.

Suggested Citation

  • Thi-Hai-Yen Vuong & Hai-Long Nguyen & Tan-Minh Nguyen & Hoang-Trung Nguyen & Thai-Binh Nguyen & Ha-Thanh Nguyen, 2024. "NOWJ at COLIEE 2023: Multi-task and Ensemble Approaches in Legal Information Processing," The Review of Socionetwork Strategies, Springer, vol. 18(1), pages 145-165, April.
    • Handle: RePEc:spr:trosos:v:18:y:2024:i:1:d:10.1007_s12626-024-00157-3
    DOI: 10.1007/s12626-024-00157-3
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