NOWJ at COLIEE 2023 -- Multi-Task and Ensemble Approaches in Legal Information Processing
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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Methods
Adam •
AdamW •
Attention Dropout •
BERT •
Dense Connections •
Dilated Sliding Window Attention •
Dropout •
GELU •
Global and Sliding Window Attention •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
Longformer •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Sliding Window Attention •
Softmax •
Weight Decay •
WordPiece
