Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches
<|reference_start|>Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches: Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident. 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. 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 -- providing the "recipe" for the optimal setups.<|reference_end|>
@article{dementieva2024ukrainian,
    title={Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge
  Transfer Approaches},
    author={Daryna Dementieva, Valeriia Khylenko and Georg Groh},
    journal={arXiv preprint arXiv:2404.02043},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02043},
    primaryClass={cs.CL cs.AI}
}