JNLP Team: Deep Learning for Legal Processing in COLIEE 2020

TL;DR

This paper introduces deep learning methods for legal retrieval and question-answering in COLIEE 2020, leveraging pre-training to address data scarcity and improve performance in legal information processing.

Contribution

It presents a novel deep learning framework with pre-training for legal tasks, enhancing accuracy and adaptability in legal information retrieval and QA.

Findings

Effective legal retrieval and QA performance demonstrated

Pre-training reduces data scarcity issues

Approach applicable to other domain-specific problems

Abstract

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.