LexC-Gen, a method proposed by researchers at Brown University, addresses data scarcity in low-resource languages using bilingual lexicons and large language models (LLMs). It generates labeled task data for low-resource languages by leveraging LLMs and bilingual lexicons, achieving performance comparable to gold data in sentiment analysis and topic classification tasks. The method offers promise in accelerating progress in NLP for underserved linguistic communities.
Data Solutions for Low-Resource Languages
Addressing the Challenge
Data scarcity in low-resource languages can be mitigated using word-to-word translations from high-resource languages. However, this approach often leads to inadequate translation coverage, widening the gap in NLP progress compared to high-resource languages.
Leveraging Lexicons for Data Augmentation
Lexicon-based cross-lingual data augmentation involves swapping words in high-resource language data with their translations from bilingual lexicons to generate data for low-resource languages. This approach is effective for various NLP tasks but faces challenges with domain specificity and performance compared to native data.
The Solution: LexC-Gen
Researchers from Brown University have proposed LexC-Gen, a method for scalable generation of low-resource-language classification task data. It leverages bilingual lexicons to create lexicon-compatible task data in high-resource languages and then translates them into low-resource languages through word translation.
Key Benefits and Outcomes
LexC-Gen outperforms baselines in sentiment analysis and topic classification tasks across 17 low-resource languages. It demonstrates superiority over all baselines, offering promise in mitigating data scarcity in low-resource languages and accelerating progress in NLP for these underserved communities.
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