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The Multilingual Corpus of World’s Constitutions (MCWC): MCWC

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Forthcoming
Publication date25/03/2024
Number of pages10
<mark>Original language</mark>English
Event The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation - Torino, Italy
Duration: 20/05/202425/05/2024
https://lrec-coling-2024.org/

Conference

Conference The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Abbreviated titleLREC-COLING 2024
Country/TerritoryItaly
CityTorino
Period20/05/2425/05/24
Internet address

Abstract

The “Multilingual Corpus of World’s Constitutions” (MCWC) is a rich resource available in English, Arabic, and Spanish, encompassing constitutions from various nations. This corpus serves as a vital asset for the NLP community, facilitating advanced research in constitutional analysis, machine translation, and cross-lingual legal studies. To ensure comprehensive coverage, for constitutions not originally available in Arabic and Spanish, we employed a fine-tuned state-of-the-art machine translation model. 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 MCWC improves accuracy, highlighting the significance of such a legal corpus for NLP and Machine Translation. MCWC’s diverse multilingual content and commitment to data quality contribute to advancements in legal text analysis within the NLP community, facilitating exploration of constitutional texts and multilingual data analysis.