Generating SQL statements from natural language queries: A multitask learning approach

Abstract

NL2SQL advocates an idea of helping engineers and/or end users generate SQL statements from natural language queries. However, it still remains a strong challenge in improving its precision and scalability. This paper introduces MultiSQL, a multitask deep learning approach to performing NL2SQL. MultiSQL unifies the task representations and trains a model in parallel on multiple tasks, including NL2SQL, machine translation, etc. It employs a multitask question-answering network for jointly learning all tasks and transferring knowledge among tasks. We have evaluated MultiSQL on two query datasets: WikiSQL (an open sourced dataset) and CnSQL (a Chinese dataset we created). The evaluation results clearly show the effectiveness of MultiSQL. In particular, the accuracies achieved by MultiSQL approximate those achieved by the state-of-the-art NL2SQL methods on WikiSQL, and its accuracy is 78%, which is 17% higher than the "Chinese2English + NL2SQL" method on CnSQL.