Neural Machine Translation from Natural Language into SQL with state-of-the-art Deep Learning methods

Abstract

Reading text, identifying key ideas, summarizing, making connections and other tasks that require comprehension and context are easy tasks for humans but training a computer to perform these tasks is a challenge. Recent advances in deep learning make it possible to interpret text effectively and achieve high performance results across natural language tasks. Interacting with relational databases trough natural language enables users of any background to query and analyze a huge amount of data in a user-friendly way. This paper summaries major challenges and different approaches in the context of Natural Language Interfaces to Databases (NLIDB). A state-ofthe- art language translation model developed by Google named Transformer is used to translate natural language queries into structured queries to simplify the interaction between users and relational database systems.