The “Multilingual Corpus of World’s Constitutions” (MCWC) serves as a valuable resource for the NLP community, offering a comprehensive collection of constitutions from around the world. Its focus on data quality and breadth of coverage enables advanced research in constitutional analysis, machine translation, and cross-lingual legal studies. The MCWC prepares its data to ensure high quality and minimal noise, while also providing valuable mappings of constitutions to their respective countries and continents, facilitating comparative analysis. Notably, the corpus offers pairwise sentence alignments across languages, supporting machine translation experiments. We utilise a leading Machine Translation model, fine-tuned on the MCWC to achieve accurate and context-aware translations. Additionally, we introduce an independent Machine Translation model as a comparative baseline. Fine-tuning the model on the MCWC improves accuracy, highlighting the significance of such a legal corpus for NLP and Machine Translation. The MCWC’s rich multilingual content and rigorous data quality standards raise the bar for legal text analysis and inspire innovation in the NLP community, opening new avenues for studying constitutional texts and multilingual data analysis.

Data anonymisation is often required to comply with regulations when transfering information across departments or entities. However, the risk is that this procedure can distort the data and jeopardise the models built on it. Intuitively, the process of training an NLP model on anonymised data may lower the performance of the resulting model when compared to a model trained on non-anonymised data. In this paper, we investigate the impact of de-identification on the performance of nine downstream NLP tasks. We focus on the anonymisation and pseudonymisation of personal names and compare six different anonymisation strategies for two state-of-the-art pre-trained models. Based on these experiments, we formulate recommendations on how the de-identification should be performed to guarantee accurate NLP models. Our results reveal that de-identification does have a negative impact on the performance of NLP models, but this impact is relatively low. We also find that using pseudonymisation techniques involving random names leads to better performance across most tasks.