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.
@article{arxiv.2103.14540,
title = {NL-EDIT: Correcting semantic parse errors through natural language interaction},
author = {Ahmed Elgohary and Christopher Meek and Matthew Richardson and Adam Fourney and Gonzalo Ramos and Ahmed Hassan Awadallah},
journal= {arXiv preprint arXiv:2103.14540},
year = {2021}
}