  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.
