Innovative SQL Query Generator Using an Arabic Language Description

This paper presents the development of an advanced system designed to efficiently process Arabic queries in today’s data-driven landscape. By using the T5 sequence-to-sequence model developed by Google and fine-tuning it specifically for transforming textual and verbal input into SQL queries, the system becomes adept at accurately comprehending and interpreting Arabic queries. The resulting system serves as a user-friendly tool that automates the generation of SQL queries. Users can input their queries in written or recorded Arabic utterances, eliminating the need for manual translation and query construction. This involved fine-tuning T5 models on a SQL dataset, splitting the dataset, tokenizing it, and setting training parameters. The implementation phase included loading the fine-tuned model, which incorporated PICARD to generate valid queries effectively. This paper also explores the impact of Arabic translation on the performance and accuracy of the model. The optimal testing on test set accuracy achieved was 63.11%, the real-world testing accuracy: T5-base without PICARD scored 56.96% and 51.36% on English and Arabic questions, respectively, while T5-large with PIARCD scored 77.74% and 70.34% on English and Arabic questions.