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

arXiv: https://arxiv.org/abs/2306.04903 · HTML: https://mlsanity.com/paper/2306.04903/

## 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 (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Yes, BM25 is a Strong Baseline for Legal Case Retrieval](https://mlsanity.com/paper/2105.05686/) (2021)
- [Attentive Deep Neural Networks for Legal Document Retrieval](https://mlsanity.com/paper/2212.13899/) (2022)
- [Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task](https://mlsanity.com/paper/2205.15172/) (2022)

## Built on by (most cited first, 4 of 4)

- [Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task](https://mlsanity.com/paper/2309.05501/) (2023)
- [Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models](https://mlsanity.com/paper/2403.18093/) (2024)
- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)
- [GPTs and Language Barrier: A Cross-Lingual Legal QA Examination](https://mlsanity.com/paper/2403.18098/) (2024)

## 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.