Abstract: NLIDB (natural language interface to database) provides a new form to access databases with barrier-free text query, which reduces the burdens for users to learn the SQL (structured query language). Because of its great application value, NLIDB has attracted much attention in the field of scientific research and commercial in recent years. Most of the current mature NLIDB systems are based on classical natural language processing technologies, which depend on rule-based approaches to realize the transformation from natural language questions to SQL. But these approaches have poor ability to generalize. Deep learning models have advantages on distributed and high-level representation learning, which are competent for semantic feature mining in natural language. Therefore, the application of deep learning technology in NLIDB has gradually become a hot research topic nowadays. This paper provides a systematic review of the NLIDB researches based on deep learning in recent years. The main contributions are as follows: firstly, according to the decoding method, we sort out existing deep learning-based NLIDB models into 4 categories, and state the advantage and the weakness respectively; secondly, we summarize 7 common assist techniques in the implementations of the NLIDB models; thirdly, we propose the problems remaining to be solved and put forward the relevant directions for future researches.

Key words: natural language interface, database, SQL, deep learning, semantic parsing