@inproceedings{aushev-etal-2025-ragulator,
title = "{RAG}ulator: Effective {RAG} for Regulatory Question Answering",
author = "Aushev, Islam and
Kratkov, Egor and
Nikoalev, Evgenii and
Glinskii, Andrei Vladimirovich and
Krikunov, Vasilii and
Panchenko, Alexander and
Konovalov, Vasily and
Belikova, Julia",
editor = "Gokhan, Tuba and
Wang, Kexin and
Gurevych, Iryna and
Briscoe, Ted",
booktitle = "Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.regnlp-1.18/",
pages = "114--120",
abstract = "Regulatory Natural Language Processing (RegNLP) is a multidisciplinary domain focused on facilitating access to and comprehension of regulatory regulations and requirements. This paper outlines our strategy for creating a system to address the Regulatory Information Retrieval and Answer Generation (RIRAG) challenge, which was conducted during the RegNLP 2025 Workshop. The objective of this competition is to design a system capable of efficiently extracting pertinent passages from regulatory texts (ObliQA) and subsequently generating accurate, cohesive responses to inquiries related to compliance and obligations. Our proposed method employs a lightweight BM25 pre-filtering in retrieving relevant passages. This technique efficiently shortlisting candidates for subsequent processing with Transformer-based embeddings, thereby optimizing the use of resources."
}
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<abstract>Regulatory Natural Language Processing (RegNLP) is a multidisciplinary domain focused on facilitating access to and comprehension of regulatory regulations and requirements. This paper outlines our strategy for creating a system to address the Regulatory Information Retrieval and Answer Generation (RIRAG) challenge, which was conducted during the RegNLP 2025 Workshop. The objective of this competition is to design a system capable of efficiently extracting pertinent passages from regulatory texts (ObliQA) and subsequently generating accurate, cohesive responses to inquiries related to compliance and obligations. Our proposed method employs a lightweight BM25 pre-filtering in retrieving relevant passages. This technique efficiently shortlisting candidates for subsequent processing with Transformer-based embeddings, thereby optimizing the use of resources.</abstract>
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%0 Conference Proceedings
%T RAGulator: Effective RAG for Regulatory Question Answering
%A Aushev, Islam
%A Kratkov, Egor
%A Nikoalev, Evgenii
%A Glinskii, Andrei Vladimirovich
%A Krikunov, Vasilii
%A Panchenko, Alexander
%A Konovalov, Vasily
%A Belikova, Julia
%Y Gokhan, Tuba
%Y Wang, Kexin
%Y Gurevych, Iryna
%Y Briscoe, Ted
%S Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F aushev-etal-2025-ragulator
%X Regulatory Natural Language Processing (RegNLP) is a multidisciplinary domain focused on facilitating access to and comprehension of regulatory regulations and requirements. This paper outlines our strategy for creating a system to address the Regulatory Information Retrieval and Answer Generation (RIRAG) challenge, which was conducted during the RegNLP 2025 Workshop. The objective of this competition is to design a system capable of efficiently extracting pertinent passages from regulatory texts (ObliQA) and subsequently generating accurate, cohesive responses to inquiries related to compliance and obligations. Our proposed method employs a lightweight BM25 pre-filtering in retrieving relevant passages. This technique efficiently shortlisting candidates for subsequent processing with Transformer-based embeddings, thereby optimizing the use of resources.
%U https://aclanthology.org/2025.regnlp-1.18/
%P 114-120
Markdown (Informal)
[RAGulator: Effective RAG for Regulatory Question Answering](https://aclanthology.org/2025.regnlp-1.18/) (Aushev et al., RegNLP 2025)
ACL
- Islam Aushev, Egor Kratkov, Evgenii Nikoalev, Andrei Vladimirovich Glinskii, Vasilii Krikunov, Alexander Panchenko, Vasily Konovalov, and Julia Belikova. 2025. RAGulator: Effective RAG for Regulatory Question Answering. In Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025), pages 114–120, Abu Dhabi, UAE. Association for Computational Linguistics.