Summary of Araspider: Democratizing Arabic-to-sql, by Ahmed Heakl et al.
AraSpider: Democratizing Arabic-to-SQL
by Ahmed Heakl, Youssef Mohamed, Ahmed B. Zaky
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR); 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 AraSpider is a groundbreaking dataset designed specifically for natural language processing (NLP) research in the Arabic-speaking community. The study compares four multilingual translation models, including ChatGPT 3.5 and SQLCoder, to evaluate their effectiveness in translating English to Arabic. Moreover, two models were assessed for generating SQL queries from Arabic text. Notably, back translation significantly improved the performance of both top-performing models, with ChatGPT 3.5 demonstrating high-quality translations and SQLCoder exceling in text-to-SQL tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about a special dataset called AraSpider that helps improve language processing in Arabic-speaking countries. The researchers tested four different models to see how well they could translate English into Arabic. They also checked how good these models were at generating SQL queries from Arabic text. The results showed that using a certain technique, called back translation, made the models perform better. This is important because it can help Arabic-speaking researchers do their work more easily. |
Keywords
* Artificial intelligence * Natural language processing * Nlp * Translation
