论文标题
FFR V1.1：FON-FRENCH神经机器翻译
FFR v1.1: Fon-French Neural Machine Translation
论文作者
论文摘要
在世界各地，尤其是在非洲，研究人员正在努力建立神经机器翻译（NMT）系统，以帮助应对非洲的语言障碍，这是一个超过2000种不同语言的大陆。但是，非洲语言的低保养，音调和音调复杂性是面临的主要问题。 FFR项目是创建从FON（一种非常低的资源和音调语言）到法语，用于研究和公众使用的强大翻译模型的重要一步。在本文中，我们介绍了FFR数据集，FFR数据集（FON-FON-FRENCH翻译的语料库），描述了音调编码过程，并介绍了在数据集中训练的FFR V1.1模型。该数据集和模型可在https://github.com/ bonaventuredossou/ffr-v1上公开提供，以促进协作和可重复性。
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.
