Summary of Cross-lingual Text Classification Transfer: the Case Of Ukrainian, by Daryna Dementieva et al.
Cross-lingual Text Classification Transfer: The Case of Ukrainian
by Daryna Dementieva, Valeriia Khylenko, Georg Groh
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract proposes a solution to address the imbalance in data availability across languages in NLP text classification. It highlights the lack of Ukrainian corpora for typical text classification tasks and explores cross-lingual knowledge transfer methods using large multilingual encoders, translation systems, LLMs, and language adapters. The paper tests these approaches on three text classification tasks: toxicity classification, formality classification, and natural language inference (NLI). It provides a “recipe” for the optimal setup for each task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to help develop NLP models more fairly by sharing knowledge between languages. The main problem is that there aren’t many labeled datasets in Ukrainian, making it hard to classify texts correctly. To solve this issue, scientists are using special language tools and training systems to transfer knowledge from one language to another. They tested these methods on three tasks: identifying toxic language, determining if text is formal or informal, and understanding natural language relationships. The goal is to make NLP models better and more useful for people who don’t speak English. |
Keywords
» Artificial intelligence » Classification » Inference » Nlp » Text classification » Translation
