FFR v1.1: Fon-French Neural Machine Translation

By Bonaventure F. P. Dossou, Chris C. Emezue. 2020

28 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing, Machine Translation

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Citations in the corpus

Beyond English-Centric Multilingual Machine Translation (2020, methodology): “, 2020), Fon (Emezue and Dossou, 2020), Igbo (Ezeani et al.”
Building Machine Translation Systems for the Next Thousand Languages (2022, background): “…& Puttkammer, 2014), Setswana and Sepedi (Marivate et al., 2020), Yorùbá (Adelani et al., 2021b;a), Oshiwambo (Nekoto et al., 2022), Igbo (Ezeani et al., 2020), Zulu (Mabuya et al., 2021), Twi (Azunre et al., 2021b), Gbe (Hacheme, 2021), Bambara (Tapo et al., 2021), and Fon (Emezue & Dossou, 2020).”