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, Ha-Thanh Nguyen

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.