ukr-detect/ukr-formality-dataset-translated-gyafc

Ukrainian Formality Dataset (translated)

We obtained the first of its kind Ukrainian Formality Classification dataset by trainslating English GYAFC data.

Dataset formation:

1. English data source: https://aclanthology.org/N18-1012/
2. Translation into Ukrainian language using model: https://huggingface.co/facebook/nllb-200-distilled-600M
3. Additionally, the dataset was balanced.

Labels: 0 - informal, 1 - formal.

Load dataset:

from datasets import load_dataset
dataset = load_dataset("ukr-detect/ukr-formality-dataset-translated-gyafc") 

Citation

@inproceedings{dementieva-etal-2025-cross,
    title = "Cross-lingual Text Classification Transfer: The Case of {U}krainian",
    author = "Dementieva, Daryna  and
      Khylenko, Valeriia  and
      Groh, Georg",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.coling-main.97/",
    pages = "1451--1464",
    abstract = "Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident. To support further fair development of NLP models, exploring the possibilities of effective knowledge transfer to new languages is crucial. Ukrainian, in particular, stands as a language that still can benefit from the continued refinement of cross-lingual methodologies. Due to our knowledge, there is a tremendous lack of Ukrainian corpora for typical text classification tasks, i.e., different types of style, or harmful speech, or texts relationships. However, the amount of resources required for such corpora collection from scratch is understandable. In this work, we leverage the state-of-the-art advances in NLP, exploring cross-lingual knowledge transfer methods avoiding manual data curation: large multilingual encoders and translation systems, LLMs, and language adapters. We test the approaches on three text classification tasks{---}toxicity classification, formality classification, and natural language inference (NLI){---}providing the {\textquotedblleft}recipe{\textquotedblright} for the optimal setups for each task."
}