NL-EDIT: Correcting semantic parse errors through natural language interaction
This addresses the challenge of improving semantic parse accuracy interactively for users, though it appears incremental as it builds on existing parsers.
The paper tackles the problem of correcting errors in semantic parsing by introducing NL-EDIT, a model that uses natural language feedback to generate edits for initial parses, boosting text-to-SQL parser accuracy by up to 20% with one correction turn.
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.