SQL Generation from Natural Language Using Supervised Learning and Recurrent Neural Networks

Databases store a vast amount of today’s data and information, and to access that data users are required to have command over SQL or equivalent interface language. Hence, using a system that can convert a natural language to equivalent SQL query would make the data more accessible. In this sense, building natural language interfaces to relational databases is an important and challenging problem in natural language processing (NLP) and a widely studied field, and found recently momentum again due to the introduction of large-scale Datasets. In this paper, we present our approach based on word embedding and Recurrent Neural Networks (RNN), precisely on Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) cells. We present also the DataSet used for training and testing our models, based on WikiSQL, and finally we show where we arrived in terms of accuracy.