AGE: Amharic, Ge’ez and English Parallel Dataset

Henok Biadglign Ademtew, Mikiyas Girma Birbo

Keywords: Machine Translation, Pretrained language model

Abstract: African languages are not well-represented in Natural Language Processing (NLP). The main reason is a lack of resources for training models. Low-resource languages, such as Amharic and Ge'ez, cannot benefit from modern NLP methods because of the lack of high-quality datasets. This paper presents AGE, an open-source tripartite alignment of Amharic, Ge'ez, and English parallel dataset. Additionally, we introduced a novel, 1,000 Ge'ez-centered sentences sourced from areas such as news and novels. Furthermore, we developed a model from a multilingual pre-trained language model, which brings 12.29 and 30.66 for English-Ge'ez and Ge'ez to English, respectively, and 9.39 and 12.29 for Amharic-Ge'ez and Ge'ez-Amharic respectively.

Submission Number: 30