  We explore using T5 (Raffel et al. (2019)) to directly translate natural
language questions into SQL statements. General purpose natural language that
interfaces to information stored within databases requires flexibly translating
natural language questions into database queries. The best performing
text-to-SQL systems approach this task by first converting questions into an
intermediate logical form (LF) (Lyu et al. (2020)). While LFs provide a
convenient intermediate representation and simplify query generation, they
introduce an additional layer of complexity and annotation requirements.
However, weakly supervised modeling that directly converts questions to SQL
statements has proven more difficult without the scaffolding provided by LFs
(Min et al. (2019)). We approach direct conversion of questions to SQL
statements using T5 (Raffel et al. (2019)), a pre-trained textto-text
generation model, modified to support pointer-generator style decoding (See et
al. (2017)). We explore using question augmentation with table schema
information and the use of automatically generated silver training data. The
resulting model achieves 90.5% execution accuracy on the WikiSQL (Zhong et al.
(2017)) test data set, a new state-of-the-art on weakly supervised SQL
generation. The performance improvement is 6.6% absolute over the prior
state-of-the-art (Min et al. (2019)) and approaches the performance of
state-ofthe-art systems making use of LFs.
