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UdS-LSV/menyo20k_mt

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Use restrictions

The declared licence (cc-by-nc-4.0) is classified as having use restrictions.

Declared licence: cc-by-nc-4.0, from the tagEvidence: README.md at 76b2db6
Licence files: No licence file at the top level of the repositoryEvidence: file list at 76b2db6
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

translation

Citation text from the card

@inproceedings{adelani-etal-2021-effect,
    title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation",
    author = "Adelani, David  and
      Ruiter, Dana  and
      Alabi, Jesujoba  and
      Adebonojo, Damilola  and
      Ayeni, Adesina  and
      Adeyemi, Mofe  and
      Awokoya, Ayodele Esther  and
      Espa{\~n}a-Bonet, Cristina",
    booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)",
    month = aug,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2021.mtsummit-research.6",
    pages = "61--75",
    abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test s

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dataset_licence({ "id": "UdS-LSV/menyo20k_mt" })
signal: "restrictive"
stated_licence: "cc-by-nc-4.0"
sources: 0