General Data Protection Regulation (GDPR) becomes a standard law for data protection in many countries. Currently, twelve countries adopt the regulation and establish their GDPR-like regulation. However, to evaluate the differences and similarities of these GDPR-like regulations is time-consuming and needs a lot of manual effort from legal experts. Moreover, GDPR-like regulations from different countries are written in their languages leading to a more difficult task since legal experts who know both languages are essential. In this paper, we investigate a simple natural language processing (NLP) approach to tackle the problem. We first extract chunks of information from GDPR-like documents and form structured data from natural language. Next, we use NLP methods to compare documents to measure their similarity. Finally, we manually label a small set of data to evaluate our approach. The empirical result shows that the BERT model with cosine similarity outperforms other baselines. Our data and code are publicly available.
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