@inproceedings{justin-etal-2025-yodiv3,
    title = "{Y}odi{V}3: {NLP} for {T}ogolese Languages with Eyaa-Tom Dataset and the Lom Metric",
    author = "Justin, Bakoubolo Essowe  and
      Xegbe, Kodjo Fran{\c{c}}ois  and
      Essuman, Catherine Nana Nyaah  and
      Samuel, Afola Kossi Mawou{\'e}na",
    editor = "Lignos, Constantine  and
      Abdulmumin, Idris  and
      Adelani, David",
    booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.africanlp-1.20/",
    pages = "143--149",
    ISBN = "979-8-89176-257-2",
    abstract = "Most of the 40+ languages spoken in Togo are severely under-represented in Natural Language Processing (NLP) resources. We present YodiV3, a comprehensive approach to developing NLP for ten Togolese languages (plus two major lingua francas) covering machine translation, speech recognition, text-to-speech, and language identification. We introduce Eyaa-Tom, a new multi-domain parallel corpus (religious, healthcare, financial, etc.) for these languages. We also propose the Lom metric, a scoring framework to quantify the AI-readiness of each language in terms of available resources. Our experiments demonstrate that leveraging large pretrained models (e.g.NLLB for translation, MMS for speech) with YodiV3 leads to significant improvements in low-resource translation and speech tasks. This work highlights the impact of integrating diverse data sources and pretrained models to bootstrap NLP for under-served languages, and outlines future steps for expanding coverage and capability."
}