QBR: A Question-Bank-Based Approach to Fine-Grained Legal Knowledge Retrieval for the General Public
This addresses the challenge for untrained users in accessing legal information, though it appears incremental as it builds on existing retrieval methods with a novel medium.
The paper tackles the problem of legal knowledge retrieval for the general public by proposing QBR, a question-bank-based approach that bridges the knowledge gap between technical legal content and laypersons, resulting in more accurate, efficient, and explainable fine-grained retrieval.
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.