  Most of the world's data is stored in relational databases. Accessing these
requires specialized knowledge of the Structured Query Language (SQL), putting
them out of the reach of many people. A recent research thread in Natural
Language Processing (NLP) aims to alleviate this problem by automatically
translating natural language questions into SQL queries. While the proposed
solutions are a great start, they lack robustness and do not easily generalize:
the methods require high quality descriptions of the database table columns,
and the most widely used training dataset, WikiSQL, is heavily biased towards
using those descriptions as part of the questions.
  In this work, we propose solutions to both problems: we entirely eliminate
the need for column descriptions, by relying solely on their contents, and we
augment the WikiSQL dataset by paraphrasing column names to reduce bias. We
show that the accuracy of existing methods drops when trained on our augmented,
column-agnostic dataset, and that our own method reaches state of the art
accuracy, while relying on column contents only.
