README.md

metadata

license: unknown
task_categories:
  - translation
  - text-generation
language:
  - uk
  - en
pretty_name: ukr-multi30k
size_categories:
  - 10K<n<100K
tags:
  - common
  - multi30k
  - ukrainian

Dataset Multi30k: English-Ukrainian variation

Multi30K dataset is designed to develop multilingual multimodal researches.

Initially this dataset extends the Flickr30K dataset by adding German translations. The descriptions were collected from a crowdsourcing platform, while the translations were collected from professionally contracted translators.

We present a variation of this dataset manually translated for Ukrainian language.

Paper:

@inproceedings{saichyshyna-etal-2023-extension,
    title = "Extension {M}ulti30{K}: Multimodal Dataset for Integrated Vision and Language Research in {U}krainian",
    author = "Saichyshyna, Nataliia  and
      Maksymenko, Daniil  and
      Turuta, Oleksii  and
      Yerokhin, Andriy  and
      Babii, Andrii  and
      Turuta, Olena",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.unlp-1.7",
    pages = "54--61",
    abstract = "We share the results of the project within the well-known Multi30k dataset dedicated to improving machine translation of text from English into Ukrainian. The main task was to manually prepare the dataset and improve the translation of texts. The importance of collecting such datasets for low-resource languages for improving the quality of machine translation has been discussed. We also studied the features of translations of words and sentences with ambiguous meanings.The collection of multimodal datasets is essential for natural language processing tasks because it allows the development of more complex and comprehensive machine learning models that can understand and analyze different types of data. These models can learn from a variety of data types, including images, text, and audio, for more accurate and meaningful results.",
}