Task-Driven Neural Natural Language Interface to Database

Natural language querying offers an intuitive and user-friendly interface. A Natural Language Interface over databases, often termed as “Text-to-SQL”, involves translating a query posed in natural language into a corresponding SQL query for structured databases. A significant number of recent methodologies, anchored in the pre-trained language model and encode-decode paradigms, have been developed to address this task. Yet, existing approaches often grapple with generating accurate SQL queries, especially in scenarios that involve multiple values and intricate column calculations. In this study, we present a task-driven Text-to-SQL model. This model breaks down the SQL prediction process into specific sub-tasks based on the unique task requirements of the query. Specifically, we amalgamate structure prediction, value extraction, and column relationship prediction into a cohesive workflow. The model is designed to construct target SQL queries incrementally, with each sub-task building upon the outcomes of its predecessors. Additionally, we introduce a novel filtering mechanism to refine and re-order candidates produced during the beam search phase. We substantiate the efficacy of our model using public datasets, showcasing its adeptness in both English and Chinese contexts.