LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons (2024.findings-emnlp)
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| Challenge: | Existing word-to-word translations from labeled task data in low-resource languages have limited lexical overlap with task data. |
| Approach: | They propose a method that generates low-resource-language classification task data at scale using bilingual lexicons. |
| Outcome: | The proposed method improves on 17 low-resource languages with bilingual lexicons compared with existing models on sentiment analysis and topic classification tasks. |
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