Learning to Transform Vietnamese Natural Language Queries into SQL Commands

In the field of data management, users traditionally manipulates their data using structured query language (SQL). However, this method requires an understanding of relational database, data schema, and SQL syntax as well as the way it works. Database manipulation using natural language, therefore, is much more convenient since any normal user can interact with their data without a background of database and SQL. This is, however, really tough because transforming natural language commands into SQL queries is a challenging task in natural language processing and understanding. In this paper, we propose a novel two-phase approach to automatically analyzing and converting natural language queries into the corresponding SQL forms. In our approach, the first phase is component segmentation which identifies primary clauses in SQL such as SELECT, FROM, WHERE, ORDER BY, etc. The second phase is slot-filling that helps extract sub-components for each primary clause such as SELECT column(s), SELECT aggregation operation, etc. We carefully conducted an empirical evaluation for our method using conditional random fields (CRFs) on a medium-sized corpus of natural language queries in Vietnamese, and have achieved promising results with an average accuracy of more than 90%.