Building a natural language interface for relational databases is an important and challenging problem in natural language processing (NLP). It requires a system that can understand natural language problems and generate corresponding SQL queries. In this article, we propose the idea of using type information and database content to better understand the rare entities and numbers in natural language problems to improve the model SyntaxSQLNet as the latest technology in Text-to-SQL tasks. We also showed the global architecture and technologies that can be used to implement our neural network (NN) model Text2SQLNet, and integrated our ideas, including using type information to better understand rare entities and numbers in natural language problems. If the format of the user query is incorrect, we can also use the database content to better understand the user query. The realization of this idea can further improve the performance in Text-to-SQL tasks.
About the study
“Relational databases store a vast amount of today’s information and provide the foundation of applications such as medical records (Hillestad et al., 2005), financial markets (Beck and al., 2000), and customer relations management (Ngai et al., 2009). However, accessing relational databases requires an understanding of query languages such as SQL, which, while powerful, is difficult to master. Natural language interfaces (NLI), a research area at the intersection of natural language processing and human- computer interactions, seeks to provide means for humans to interact with computers through the use of natural language (Androutsopoulos et al., 1995). Natural language always contains ambiguities, each user can express himself in his own way.”