Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches

• URL: http://arxiv.org/abs/2404.02043v1
• Date: Tue, 2 Apr 2024 15:37:09 GMT
• Title: Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches
• Authors: Daryna Dementieva, Valeriia Khylenko, Georg Groh,
• Abstract summary: There is a tremendous lack of Ukrainian corpora for typical text classification tasks. We explore cross-lingual knowledge transfer methods avoiding manual data curation. We test the approaches on three text classification tasks.
• Score: 11.508759658889382
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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