@inproceedings{ademtew-birbo-2024-age,
    title = "{AGE}: {A}mharic, {G}e`ez and {E}nglish Parallel Dataset",
    author = "Ademtew, Henok Biadglign  and
      Birbo, Mikiyas Girma",
    editor = "Ojha, Atul Kr.  and
      Liu, Chao-hong  and
      Vylomova, Ekaterina  and
      Pirinen, Flammie  and
      Abbott, Jade  and
      Washington, Jonathan  and
      Oco, Nathaniel  and
      Malykh, Valentin  and
      Logacheva, Varvara  and
      Zhao, Xiaobing",
    booktitle = "Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.loresmt-1.14/",
    doi = "10.18653/v1/2024.loresmt-1.14",
    pages = "139--145",
    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."
}