  Despite remarkable progress in text-to-SQL semantic parsing in recent years,
the performance of existing parsers is still far from perfect. Specifically,
modern text-to-SQL parsers based on deep learning are often over-confident,
thus casting doubt on their trustworthiness when deployed for real use. In this
paper, we propose a parser-independent error detection model for text-to-SQL
semantic parsing. Using a language model of code as its bedrock, we enhance our
error detection model with graph neural networks that learn structural features
of both natural language questions and SQL queries. We train our model on
realistic parsing errors collected from a cross-domain setting, which leads to
stronger generalization ability. Experiments with three strong text-to-SQL
parsers featuring different decoding mechanisms show that our approach
outperforms parser-dependent uncertainty metrics. Our model could also
effectively improve the performance and usability of text-to-SQL semantic
parsers regardless of their architectures. (Our implementation is available at
https://github.com/OSU-NLP-Group/Text2SQL-Error-Detection)
