Summary of Lexc-gen: Generating Data For Extremely Low-resource Languages with Large Language Models and Bilingual Lexicons, by Zheng-xin Yong et al.
LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons
by Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed method, LexC-Gen, generates classification task data at scale for extremely low-resource languages. It uses bilingual lexicons to translate high-resource-language words into the target language, achieving competitive results with expert-translated gold data. The approach yields an average 5.6 and 8.9 points improvement over existing methods on sentiment analysis and topic classification tasks, respectively. LexC-Gen’s conditioning on bilingual lexicons is key to its success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LexC-Gen helps solve a big problem: getting computers to understand words in languages where there isn’t much data available. The method takes labeled task data from high-resource languages and translates it into the target language using bilingual dictionaries. This allows for more accurate classification tasks like sentiment analysis and topic classification. By generating lots of training data, LexC-Gen helps bridge the gap between open-source and commercial AI models. |
Keywords
* Artificial intelligence * Classification
