FFR v1.1: Fon-French Neural Machine Translation

Chris Chinenye Emezue, Femi Pancrace Bonaventure Dossou

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.
Anthology ID:
2020.winlp-1.21
Volume:
Proceedings of the The Fourth Widening Natural Language Processing Workshop
Month:
July
Year:
2020
Address:
Seattle, USA
Venue:
WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
83–87
Language:
URL:
https://aclanthology.org/2020.winlp-1.21
DOI:
10.18653/v1/2020.winlp-1.21
Bibkey:
Cite (ACL):
Chris Chinenye Emezue and Femi Pancrace Bonaventure Dossou. 2020. FFR v1.1: Fon-French Neural Machine Translation. In Proceedings of the The Fourth Widening Natural Language Processing Workshop, pages 83–87, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
FFR v1.1: Fon-French Neural Machine Translation (Emezue & Dossou, WiNLP 2020)
Copy Citation:
Search