@inproceedings{kuzhuget-etal-2024-enhancing,
    title = "Enhancing Tuvan Language Resources through the {FLORES} Dataset",
    author = "Kuzhuget, Ali  and
      Mongush, Airana  and
      Oorzhak, Nachyn-Enkhedorzhu",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Ninth Conference on Machine Translation",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.wmt-1.46/",
    doi = "10.18653/v1/2024.wmt-1.46",
    pages = "593--599",
    abstract = "FLORES is a benchmark dataset designed for evaluating machine translation systems, partic- ularly for low-resource languages. This paper, conducted as a part of Open Language Data Ini- tiative (OLDI) shared task, presents our contri- bution to expanding the FLORES dataset with high-quality translations from Russian to Tu- van, an endangered Turkic language. Our ap- proach combined the linguistic expertise of na- tive speakers to ensure both accuracy and cul- tural relevance in the translations. This project represents a significant step forward in support- ing Tuvan as a low-resource language in the realm of natural language processing (NLP) and machine translation (MT)."
}