FFR v1.1: Fon-French Neural Machine Translation
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 at https://github.com/ bonaventuredossou/ffr-v1, to promote collaboration and reproducibility.
Keywords
Cite
@article{arxiv.2006.09217,
title = {FFR v1.1: Fon-French Neural Machine Translation},
author = {Bonaventure F. P. Dossou and Chris C. Emezue},
journal= {arXiv preprint arXiv:2006.09217},
year = {2020}
}
Comments
Accepted for publication at the Widening Natural Language Processing (WiNLP) Workshop, The 58th Annual Meeting of the Association for Computational Linguistics, 2020