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Automated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study
Abualhaija, Sallam; Arora, Chetan; Sleimi, Amin et al.
2022In Proceedings of the 30th IEEE International Requirements Engineering Conference (RE'22), Melbourne, Australia 15-19 August 2022
Peer reviewed
 

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Keywords :
Requirements Engineering; Regulatory Compliance; Natural Language Processing (NLP); Question Answering; Language Models (LMs); BERT
Abstract :
[en] Software systems are increasingly subject to regulatory compliance. Extracting compliance requirements from regulations is challenging. Ideally, locating compliance-related information in a regulation requires a joint effort from requirements engineers and legal experts, whose availability is limited. However, regulations are typically long documents spanning hundreds of pages, containing legal jargon, applying complicated natural language structures, and including cross-references, thus making their analysis effort-intensive. In this paper, we propose an automated question-answering (QA) approach that assists requirements engineers in finding the legal text passages relevant to compliance requirements. Our approach utilizes large-scale language models fine-tuned for QA, including BERT and three variants. We evaluate our approach on 107 question-answer pairs, manually curated by subject-matter experts, for four different European regulatory documents. Among these documents is the general data protection regulation (GDPR) – a major source for privacy-related requirements. Our empirical results show that, in ~94% of the cases, our approach finds the text passage containing the answer to a given question among the top five passages that our approach marks as most relevant. Further, our approach successfully demarcates, in the selected passage, the right answer with an average accuracy of ~ 91%.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation
Disciplines :
Computer science
Author, co-author :
Abualhaija, Sallam  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV
Arora, Chetan;  Deakin University
Sleimi, Amin;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Briand, Lionel ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV
External co-authors :
yes
Language :
English
Title :
Automated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study
Publication date :
2022
Event name :
30th IEEE International Requirements Engineering Conference
Event date :
from 15-0802022 to 19-08-2022
Main work title :
Proceedings of the 30th IEEE International Requirements Engineering Conference (RE'22), Melbourne, Australia 15-19 August 2022
Publisher :
IEEE
Pages :
39-50
Peer reviewed :
Peer reviewed
FnR Project :
FNR11801776 - Semantic Metadata And Compliance Rule Extraction From Legal Texts, 2017 (01/01/2018-30/04/2021) - Lionel Briand
Funders :
FNR - Luxembourg National Research Fund [LU]
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