AraSpider: Democratizing Arabic-to-SQL
Ahmed Heakl Youssef Mohamed Ahmed B. Zaky
This study presents AraSpider, the first Arabic version of the Spider
dataset, aimed at improving natural language processing (NLP) in the
Arabic-speaking community. Four multilingual translation models were tested for
their effectiveness in translating English to Arabic. Additionally, two models
were assessed for their ability to generate SQL queries from Arabic text. The
results showed that using back translation significantly improved the
performance of both ChatGPT 3.5 and SQLCoder models, which are considered top
performers on the Spider dataset. Notably, ChatGPT 3.5 demonstrated
high-quality translation, while SQLCoder excelled in text-to-SQL tasks. The
study underscores the importance of incorporating contextual schema and
employing back translation strategies to enhance model performance in Arabic
NLP tasks. Moreover, the provision of detailed methodologies for
reproducibility and translation of the dataset into other languages highlights
the research's commitment to promoting transparency and collaborative knowledge
sharing in the field. Overall, these contributions advance NLP research,
empower Arabic-speaking researchers, and enrich the global discourse on
language comprehension and database interrogation.