@inproceedings{el-haj-ezzini-2024-multilingual,
    title = "The Multilingual Corpus of World{'}s Constitutions ({MCWC})",
    author = "El-Haj, Mo  and
      Ezzini, Saad",
    editor = "Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Mubarak, Hamdy  and
      Ali, Mona  and
      Elsayed, Tamer",
    booktitle = "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.osact-1.7",
    pages = "57--66",
    abstract = "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.",
}

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.
El-Haj, Mo Ezzini, Saad
The Multilingual Corpus of World's Constitutions (MCWC)