Year: 2025
Type: conference-paper
Abstract: Legal question answering requires accurate retrieval of relevant laws, yet the significant writing style gap between user queries and legal provisions poses a major challenge. Existing datasets and retrieval methods often struggle to capture the complexity of legal language, limiting retrieval effectiveness. In this study, we introduce the Legal Query-to-Provision Retrieval (LQPR) task and construct Query2Provision (Q2P), a dataset designed to enhance law retrieval by incorporating diverse case scenarios and linguistic structures representative of real-world legal inquiries. To address the style disparity, we propose a style translation approach that transforms informal user queries into a more formal legal tone and simplifies complex legal provisions for better alignment. Our experiments demonstrate that integrating writing style transformation significantly improves retrieval performance. The dataset is available at https://github.com/ntunlplab/Query2Provision
Authors: Szu-Ju Chen, Jing Jin, Sheng-Lun Wei, Chien-Hung Chen, Hsin-Hsi Chen
Institution: National Taiwan University
Language: English
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4412378285