  A significant amount of the world's knowledge is stored in relational
databases. However, the ability for users to retrieve facts from a database is
limited due to a lack of understanding of query languages such as SQL. We
propose Seq2SQL, a deep neural network for translating natural language
questions to corresponding SQL queries. Our model leverages the structure of
SQL queries to significantly reduce the output space of generated queries.
Moreover, we use rewards from in-the-loop query execution over the database to
learn a policy to generate unordered parts of the query, which we show are less
suitable for optimization via cross entropy loss. In addition, we will publish
WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL
queries distributed across 24241 tables from Wikipedia. This dataset is
required to train our model and is an order of magnitude larger than comparable
datasets. By applying policy-based reinforcement learning with a query
execution environment to WikiSQL, our model Seq2SQL outperforms attentional
sequence to sequence models, improving execution accuracy from 35.9% to 59.4%
and logical form accuracy from 23.4% to 48.3%.
