@inproceedings{tonja-etal-2024-ethiomt,
    title = "{E}thio{MT}: Parallel Corpus for Low-resource {E}thiopian Languages",
    author = "Tonja, Atnafu Lambebo  and
      Kolesnikova, Olga  and
      Gelbukh, Alexander  and
      Kalita, Jugal",
    editor = "Mabuya, Rooweither  and
      Matfunjwa, Muzi  and
      Setaka, Mmasibidi  and
      van Zaanen, Menno",
    booktitle = "Proceedings of the Fifth Workshop on Resources for African Indigenous Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.rail-1.12",
    pages = "107--114",
    abstract = "Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size of available parallel corpora in these languages, if such corpora are available at all. NLP in Ethiopian languages suffers from the same issues due to the unavailability of publicly accessible datasets for NLP tasks, including MT. To help the research community and foster research for Ethiopian languages, we introduce EthioMT {--} a new parallel corpus for 15 languages. We also create a new benchmark by collecting a dataset for better-researched languages in Ethiopia. We evaluate the newly collected corpus and the benchmark dataset for 23 Ethiopian languages using transformer and fine-tuning approaches.",
}

Recent research in natural language processing (NLP) has achieved impressive performance in tasks such as machine translation (MT), news classification, and question-answering in high-resource languages. However, the performance of MT leaves much to be desired for low-resource languages. This is due to the smaller size of available parallel corpora in these languages, if such corpora are available at all. NLP in Ethiopian languages suffers from the same issues due to the unavailability of publicly accessible datasets for NLP tasks, including MT. To help the research community and foster research for Ethiopian languages, we introduce EthioMT {--} a new parallel corpus for 15 languages. We also create a new benchmark by collecting a dataset for better-researched languages in Ethiopia. We evaluate the newly collected corpus and the benchmark dataset for 23 Ethiopian languages using transformer and fine-tuning approaches.
Tonja, Atnafu Lambebo Kolesnikova, Olga Gelbukh, Alex er Kalita, Jugal
EthioMT: Parallel Corpus for Low-resource Ethiopian Languages