Abstract
General Data Protection Regulation (GDPR) is an important framework for data protection that applies to all European Union countries. Recently, DAPRECO knowledge base (KB) which is a repository of if-then rules written in LegalRuleML as a formal logic representation of GDPR has been introduced to assist compliance checking. DAPRECO KB is, however, constructed manually and the current version does not cover all the articles in GDPR. Looking for an automated method, we present our machine translation approach to obtain a semantic parser translating the regulations in GDPR to their logic representation on DAPRECO KB. We also propose a new version of GDPR Semantic Parsing data by splitting each complex regulation into simple subparagraph-like units and re-annotating them based on published data from DAPRECO project. Besides, to improve the performance of our semantic parser, we propose two mechanisms: Sub-expression intersection and PRESEG. The former deals with the problem of duplicate sub-expressions while the latter distills knowledge from pre-trained language model BERT. Using these mechanisms, our semantic parser obtained a performance of 60.49% F1 in sub-expression level, which outperforms the baseline model by 5.68%.
M.-P. Nguyen and T.-T-.T. Nguyen—These authors contributed equally to this work.
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Acknowledgment.
This work was supported by JSPS Kakenhi Grant Number 20H04295, 20K20406, and 20K20625.
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Nguyen, MP., Nguyen, TTT., Tran, V., Nguyen, HT., Nguyen, LM., Satoh, K. (2022). Learning to Map the GDPR to Logic Representation on DAPRECO-KB. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_35
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