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