Converting Natural Language To SQL Queries Using LSTM

Abstract

bstract: In today's world, not everyone is familiar with using Structured Query Language (SQL). This makes it hard for users to understand or create complex SQL queries. What we need is an improved application with a smarter interface that can bridge the gap between novice users and databases. Databases are great at managing data, but to understand their structure, users have to learn SQL. This poses a challenge for non-experts who aren't well-versed in SQL. What they need is a system that allows them to interact with databases in natural language. The system should be capable of understanding and responding to natural language commands. To achieve this objective, we utilize a range of end-to-end deep learning models, as well as probability models like conditional random field. The ambiguity in natural language makes it exceedingly difficult to determine the exact meaning of every word, therefore it is a difficult process to map individual keywords to the description of the schema and the contents of the underlying database. Accurate predictions can help us avoid unnecessary trouble. If we apply machine learning tools, we can skip the complicated process.