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

Thi-Hai-Yen Vuong (), Hai-Long Nguyen (), Tan-Minh Nguyen (), Hoang-Trung Nguyen (), Thai-Binh Nguyen () and Ha-Thanh Nguyen ()
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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

The Review of Socionetwork Strategies, 2024, vol. 18, issue 1, 145-165

Abstract: 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.

Keywords: Legal information processing; COLIEE Competition; NOWJ Team; Multi-task Learning; Ensemble Approach (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12626-024-00157-3

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