FFR v1.1: Fon-French Neural Machine Translation

TL;DR

This paper presents FFR v1.1, a neural machine translation model and dataset for translating Fon, a low-resource tonal language, to French, addressing language barriers in Africa with publicly available resources.

Contribution

The paper introduces a new Fon-to-French translation dataset and a neural translation model, advancing research on low-resource African languages.

Findings

Dataset and model are publicly available for research and development

The FFR v1.1 model effectively translates Fon to French

The dataset captures diacritical and tonal features of Fon

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.