@inproceedings{emezue-dossou-2020-ffr,
title = "{FFR} v1.1: {F}on-{F}rench Neural Machine Translation",
author = "Emezue, Chris Chinenye and
Dossou, Femi Pancrace Bonaventure",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan and
Tsai, Alicia and
Anastasopoulos, Antonios and
Chandu, Khyathi Raghavi",
booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
month = jul,
year = "2020",
address = "Seattle, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.winlp-1.21",
doi = "10.18653/v1/2020.winlp-1.21",
pages = "83--87",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T FFR v1.1: Fon-French Neural Machine Translation
%A Emezue, Chris Chinenye
%A Dossou, Femi Pancrace Bonaventure
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F emezue-dossou-2020-ffr
%X 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.
%R 10.18653/v1/2020.winlp-1.21
%U https://aclanthology.org/2020.winlp-1.21
%U https://doi.org/10.18653/v1/2020.winlp-1.21
%P 83-87
Markdown (Informal)
[FFR v1.1: Fon-French Neural Machine Translation](https://aclanthology.org/2020.winlp-1.21) (Emezue & Dossou, WiNLP 2020)
ACL
- Chris Chinenye Emezue and Femi Pancrace Bonaventure Dossou. 2020. FFR v1.1: Fon-French Neural Machine Translation. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 83–87, Seattle, USA. Association for Computational Linguistics.