IDSpider: Indonesian Standard Dataset for Text-to-SQL

This research presents IDSpider, a new standard dataset for Text-to-SQL research in Indonesian, developed by translating the well-established Spider dataset into Indonesian. Text-to-SQL is a critical technology that bridges the gap between natural language processing (NLP) and database management systems, allowing non-technical users to retrieve data from databases using simple, natural language queries. However, most existing datasets are in English, leaving nonEnglish languages, especially Indonesian, underrepresented in the field. This paper describes the challenges encountered in translating both natural language questions and SQL queries, given the need for precision in maintaining SQL syntax and database structure integrity. We implemented a four-stage process consisting of Extraction, Translation, Cleaning, and Conversion to generate the IDSpider dataset. Furthermore, we tested the performance of the BRIDGE model on both the original Spider dataset and the translated IDSpider dataset. Results show that while the BRIDGE model performs well on Spider, its performance drops significantly when applied to IDSpider, primarily due to language differences and translation complexities. This research establishes the first benchmark for Indonesian Text-to-SQL tasks and lays the groundwork for further improvements in cross-lingual natural language interfaces for databases.