Papers › NL-EDIT: Correcting semantic parse errors through natural language interaction

NL-EDIT: Correcting semantic parse errors through natural language interaction

26 Mar 2021NAACL 2021 4arXiv:2103.14540archive 2025-07-28

Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, Ahmed Hassan Awadallah

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.

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hyan5/learning_to_simulate_nl_feedback mentioned on GitHubpytorch report

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Semantic ParsingText to SQLText-To-SQL

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