# JNLP Team: Deep Learning for Legal Processing in COLIEE 2020

arXiv: https://arxiv.org/abs/2011.08071 · HTML: https://mlsanity.com/paper/2011.08071/

## Key facts

- Cited by 14 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 4 Nov 2020.

## Built on by (most cited first, 14 of 14)

- [A Statutory Article Retrieval Dataset in French](https://mlsanity.com/paper/2108.11792/) (2021)
- [Attentive Deep Neural Networks for Legal Document Retrieval](https://mlsanity.com/paper/2212.13899/) (2022)
- [How well do SOTA legal reasoning models support abductive reasoning?](https://mlsanity.com/paper/2304.06912/) (2023)
- [JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021](https://mlsanity.com/paper/2106.13405/) (2021)
- [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)
- [Black-Box Analysis: GPTs Across Time in Legal Textual Entailment Task](https://mlsanity.com/paper/2309.05501/) (2023)
- [ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processing](https://mlsanity.com/paper/2106.13403/) (2021)
- [CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks](https://mlsanity.com/paper/2401.03551/) (2024)
- [Transformer-based Approaches for Legal Text Processing](https://mlsanity.com/paper/2202.06397/) (2022)
- [Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance](https://mlsanity.com/paper/2401.17897/) (2024)
- [Towards Automatic Comparison of Data Privacy Documents: A Preliminary Experiment on GDPR-like Laws](https://mlsanity.com/paper/2105.10117/) (2021)
- [To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment](https://mlsanity.com/paper/2202.03120/) (2022)
- [Miko Team: Deep Learning Approach for Legal Question Answering in ALQAC 2022](https://mlsanity.com/paper/2211.02200/) (2022)
- [Self-Verification is All You Need To Pass The Japanese Bar Examination](https://mlsanity.com/paper/2601.03144/) (2026)

## Abstract (arXiv)

We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020. These systems are all characterized by being pre-trained on large amounts of data before being finetuned for the specified tasks. This approach helps to overcome the data scarcity and achieve good performance, thus can be useful for tackling related problems in information retrieval, and decision support in the legal domain. Besides, the approach can be explored to deal with other domain specific problems.