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.

提出了一种利用模拟的自然语言反馈来训练交互式语义解析器的任务和新颖的反馈评估器，证明该反馈模拟器可以在低数据集情况下，生成高质量的自然语言反馈来提高特定解析器的错误纠正能力。