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

• URL: http://arxiv.org/abs/2306.04903v1
• Date: Thu, 8 Jun 2023 03:10:49 GMT
• Title: NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processing
• Authors: Thi-Hai-Yen Vuong, Hai-Long Nguyen, Tan-Minh Nguyen, Hoang-Trung Nguyen, Thai-Binh Nguyen, Ha-Thanh Nguyen
• Abstract summary: We present the NOWJ team's approach to the COLIEE 2023 Competition, which focuses on advancing legal information processing techniques. We employ state-of-the-art machine learning models and innovative approaches, such as BERT, Longformer, BM25-ranking algorithm, and multi-task learning models.
• Score: 1.5593460008414899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 tackles the four tasks in the competition, which involve 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. Although our team did not achieve state-of-the-art results, our findings provide valuable insights and pave the way for future improvements in legal information processing.

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