Summary of Ar-spider: Text-to-sql in Arabic, by Saleh Almohaimeed et al.
Ar-Spider: Text-to-SQL in Arabic
by Saleh Almohaimeed, Saad Almohaimeed, Mansour Al Ghanim, Liqiang Wang
First submitted to arxiv on: 22 Feb 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 authors introduce Ar-Spider 1, the first Arabic cross-domain text-to-SQL dataset, aiming to enable users to interact with databases in a more natural manner. The paper tackles two major challenges: schema linguistic and SQL structural challenges inherent to the Arabic language. To address these issues, the authors adopt baseline models LGESQL and S2SQL, testing them with cross-lingual models to alleviate linking challenges. The baselines demonstrate decent single-language performance on Ar-Spider, achieving 62.48% for S2SQL and 65.57% for LGESQL. To improve Arabic text-to-SQL performance, the authors propose a context similarity relationship (CSR) approach, resulting in a significant increase of about 1.52% for S2SQL and 1.06% for LGESQL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset that helps people interact with databases in Arabic, which is important because most current systems only work in English. The authors also find ways to make the system better at understanding Arabic language and SQL commands. They test different models on their new dataset and show that some models do better than others when dealing with Arabic text-to-SQL tasks. |
