Skip to main content Skip to main navigation


An End-to-end Neural Natural Language Interface for Databases

Prasetya Utama; Nathaniel Weir; Fuat Basik; Carsten Binnig; Ugur Çetintemel; Benjamin Hättasch; Amir Ilkhechi; Shekar Ramaswamy; Arif Usta
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1804.00401, Pages 0-10, arXiv, 2018.


This paper describes DBPal, a new system to translate natural language utterances into SQL statements using a neural machine translation model. While other recent approaches use neural machine translation to implement a Natural Language Interface to Databases (NLIDB), existing techniques rely on supervised learning with manually curated training data, which results in substantial overhead for supporting each new database schema. In order to avoid this issue, DBPal implements a novel training pipeline based on weak supervision that synthesizes all training data from a given database schema. In our evaluation, we show that DBPal can outperform existing rule-based NLIDBs while achieving comparable performance to other NLIDBs that leverage deep neural network models without relying on manually curated training data for every new database schema.

Weitere Links