Retrieving the Right Law: Enhancing Legal Search with Style Translation
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
- Hsin‐Hsi Chen (ORCID: https://orcid.org/0000-0001-9757-9423)
- 精文 米津
- Chien-Hung Chen (ORCID: https://orcid.org/0009-0008-0690-5181)
- Szu-Ju Chen
- Sheng-Lun Wei
Institutions
- National Taiwan University (TW)
Publication Details
- Published
- 2025-07-13
- DOI
- https://doi.org/10.1145/3726302.3730246
- Primary Topic
- Artificial Intelligence in Law
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministry of Education, India
- National Science and Technology Council