  We study semantic parsing in an interactive setting in which users correct
errors with natural language feedback. We present NL-EDIT, a model for
interpreting natural language feedback in the interaction context to generate a
sequence of edits that can be applied to the initial parse to correct its
errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL
parsers by up to 20% with only one turn of correction. We analyze the
limitations of the model and discuss directions for improvement and evaluation.
The code and datasets used in this paper are publicly available at
http://aka.ms/NLEdit.
