AmhEn: Amharic-English Large Parallel Corpus for Machine Translation

Abstract

Abstract Recently, using deep neural networks for machine translation (MT) tasks has received great attention. In order for these networks to learn abstract representations of the input and store them as continuous vectors, they need a lot of data. However, very few research studies have been conducted on low-resource languages like Amharic. The progress of an Amharic-English machine translation task in both directions is affected by the lack of clean, easy-to-find, and up-to-date parallel language corpora. This paper presents the first relatively large-scale Amharic-English parallel corpora (above 1.1 million) for machine translation tasks. We ran experiments with recurrent neural networks (RNN) and Transformer in various hyper-parameter settings to investigate the usability of our dataset. Additionally, we explore the effects of Amharic homophone character normalization on machine translation. We have released the dataset in both unnormalized and normalized forms. Our dataset is available in train, test, and validation split files.