2020

FFR V1.0: Fon-French Neural Machine Translation

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

Understand

Africa has the highest linguistic diversity in the world.

  • On account of the importance of language to communication, and the importance of reliable, powerful and accurate machine translation models in modern inter-cultural communication, there have been (and still are) efforts to create state-of-the-art translation models for the many African languages.
  • However, the low-resources, diacritical and tonal complexities of African languages are major issues facing African NLP today.
  • The FFR 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.

Built on

  • The Classification of African Languages

    Greenberg, J · 1948

    Earlier work this paper cites.

  • Neural machine translation with attention

    Original

    Tensorflow Core · 2003

    Earlier work this paper cites.

  • Sequence to Sequence Learning with Neural Networks

    Sutskever, Ilya, Vinyals, Oriol and Le, Quoc V · 2014

    Earlier work this paper cites.

Similar

  • Neural machine translation by jointly learning to align and translate

    Bahdanau, D., Cho, K. and Bengio, Y · 2015

    Cited alongside, same era.

  • Art and Life in Africa Project - Niger-Congo Languages

    Bendor-Samuel, J · 2017

    Cited alongside, same era.

Then

  • Brownlee, Jason . Deep Learning for Natural Language Processing. Machine Learning Mastery , 2017

    2017

    Later among the works it cites.

  • Intuitive Understanding of Attention Mechanism in Deep Learning

    Lamba, H · 2020

    Closest in time.

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