@inproceedings{emezue-dossou-2020-ffr,
    title = "{FFR} v1.1: {F}on-{F}rench Neural Machine Translation",
    author = "Emezue, Chris Chinenye  and
      Dossou, Femi Pancrace Bonaventure",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.winlp-1.21/",
    doi = "10.18653/v1/2020.winlp-1.21",
    pages = "83--87",
    abstract = "All over the world and especially in Africa, researchers are putting efforts into building Neural Machine Translation (NMT) systems to help tackle the language barriers in Africa, a continent of over 2000 different languages. However, the low-resourceness, diacritical, and tonal complexities of African languages are major issues being faced. The FFR project is a major step towards creating a robust translation model from Fon, a very low-resource and tonal language, to French, for research and public use. In this paper, we introduce FFR Dataset, a corpus of Fon-to-French translations, describe the diacritical encoding process, and introduce our FFR v1.1 model, trained on the dataset. The dataset and model are made publicly available, to promote collaboration and reproducibility."
}