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.

翻译：针对自然语言到SQL查询转换任务，存在若干语义和句法挑战。随着语义解析系统性能的提升，理解并修正其故障点变得愈发重要。我们探索利用自然语言反馈进行语义解析校正，提出了一种基于自回归解码器在文本到SQL任务中成功经验的新解决方案。通过分离任务的语义与句法难点，我们证明仅需一轮自然语言校正即可将文本到SQL解析器的准确率提升至多26%。此外，我们展示了T5-base模型能够在零样本、跨解析器场景下有效校正T5-large模型的错误。