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

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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

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article

Retrieving the Right Law: Enhancing Legal Search with Style Translation

Hsin‐Hsi Chen, 精文 米津, Chien-Hung Chen, Szu-Ju Chen et al.
Artificial Intelligence in Law
article

Retrieving the Right Law: Enhancing Legal Search with Style Translation

Hsin‐Hsi Chen, 精文 米津, Chien-Hung Chen, Szu-Ju Chen, Sheng-Lun Wei
article en

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

National Taiwan University (TW)
Ministry of Education, India, National Science and Technology Council
Peace, Justice and strong institutions
Openalex Percentile: Top 79%
Artificial Intelligence in Law
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