  Interactive semantic parsing based on natural language (NL) feedback, where
users provide feedback to correct the parser mistakes, has emerged as a more
practical scenario than the traditional one-shot semantic parsing. However,
prior work has heavily relied on human-annotated feedback data to train the
interactive semantic parser, which is prohibitively expensive and not scalable.
In this work, we propose a new task of simulating NL feedback for interactive
semantic parsing. We accompany the task with a novel feedback evaluator. The
evaluator is specifically designed to assess the quality of the simulated
feedback, based on which we decide the best feedback simulator from our
proposed variants. On a text-to-SQL dataset, we show that our feedback
simulator can generate high-quality NL feedback to boost the error correction
ability of a specific parser. In low-data settings, our feedback simulator can
help achieve comparable error correction performance as trained using the
costly, full set of human annotations.
