Natural Language Interface for Databases Using a Dual-Encoder Model

COLING 2018 · Ionel Alex Hosu, Radu Cristian Alex Iacob, ru, Florin Brad, Stefan Ruseti, Traian Rebedea ·

We propose a sketch-based two-step neural model for generating structured queries (SQL) based on a user{'}s request in natural language. The sketch is obtained by using placeholders for specific entities in the SQL query, such as column names, table names, aliases and variables, in a process similar to semantic parsing. The first step is to apply a sequence-to-sequence (SEQ2SEQ) model to determine the most probable SQL sketch based on the request in natural language. Then, a second network designed as a dual-encoder SEQ2SEQ model using both the text query and the previously obtained sketch is employed to generate the final SQL query. Our approach shows improvements over previous approaches on two recent large datasets (WikiSQL and SENLIDB) suitable for data-driven solutions for natural language interfaces for databases.