mlsanityData to 14 Oct 2026

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

arXiv 2306.04903 ↗First posted 8 Jun 2023cs.CLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

5 of 14 references.

References · 5

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Longformer: The Long-Document Transformercited by 3,413
  3. 2021Yes, BM25 is a Strong Baseline for Legal Case Retrievalcited by 24
  4. 2022Attentive Deep Neural Networks for Legal Document Retrievalcited by 13
  5. 2022Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Taskcited by 8

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.