Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge
  Transfer Approaches
Daryna Dementieva Valeriia Khylenko Georg Groh
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.