QBR: A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public

TL;DR

The paper introduces QBR, a novel approach using a question bank to improve fine-grained legal knowledge retrieval for the general public, addressing the gap between technical legal content and layperson understanding.

Contribution

QBR leverages a question bank to enhance knowledge embedding and retrieval accuracy, providing a more effective and explainable legal information retrieval method for non-experts.

Findings

QBR outperforms traditional methods in accuracy and efficiency.

QBR offers better interpretability and user comprehension.

Case studies demonstrate social impact in legal assistance for citizens.

Abstract

Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.