  Recent neural text-to-SQL models can effectively translate natural language
questions to corresponding SQL queries on unseen databases. Working mostly on
the Spider dataset, researchers have proposed increasingly sophisticated
solutions to the problem. Contrary to this trend, in this paper we focus on
simplifications. We begin by building DuoRAT, a re-implementation of the
state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware
or vanilla transformers as the building blocks. We perform several ablation
experiments using DuoRAT as the baseline model. Our experiments confirm the
usefulness of some techniques and point out the redundancy of others, including
structural SQL features and features that link the question with the schema.
