  In addressing the task of converting natural language to SQL queries, there
are several semantic and syntactic challenges. It becomes increasingly
important to understand and remedy the points of failure as the performance of
semantic parsing systems improve. We explore semantic parse correction with
natural language feedback, proposing a new solution built on the success of
autoregressive decoders in text-to-SQL tasks. By separating the semantic and
syntactic difficulties of the task, we show that the accuracy of text-to-SQL
parsers can be boosted by up to 26% with only one turn of correction with
natural language. Additionally, we show that a T5-base model is capable of
correcting the errors of a T5-large model in a zero-shot, cross-parser setting.
