2025/04/19 by Muhammad Rafsan Kabir, Kabir, Muhammad Rafsan, Rafeed Mohammad Sultan +11 · 2 citations
Computer Science · Social Sciences · #Artificial Intelligence in Law #Computation and Language (cs.CL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Natural Language Processing Techniques
paper · pdf · doi:10.48550/arxiv.2504.16121
openalex publication_date 2025/04/19 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
Natural Language Processing (NLP) and computational linguistic techniques are increasingly being applied across various domains, yet their use in legal and regulatory tasks remains limited. To address this gap, we develop an efficient bilingual question-answering framework for regulatory documents, specifically the Bangladesh Police Gazettes, which contain both English and Bangla text. Our approach employs modern Retrieval Augmented Generation (RAG) pipelines to enhance information retrieval and response generation. In addition to conventional RAG pipelines, we propose an advanced RAG-based approach that improves retrieval performance, leading to more precise answers. This system enables efficient searching for specific government legal notices, making legal information more accessible. We evaluate both our proposed and conventional RAG systems on a diverse test set on Bangladesh Police Gazettes, demonstrating that our approach consistently outperforms existing methods across all evaluation metrics.