MSR-LIT/Splash
Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing
This dataset helps developers who are building tools that convert natural language questions into SQL database queries. It provides examples of initial SQL queries, natural language explanations of those queries, and human feedback in natural language to correct the SQL. The goal is to improve systems that allow users to get data from databases by simply asking questions, even when the initial translation to SQL is imperfect.
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Use this if you are developing or evaluating AI models that translate natural language questions into SQL and need to simulate human interaction for correcting imperfect query results.
Not ideal if you are looking for a dataset of pre-corrected, perfect natural language to SQL pairs, or if your system does not involve interactive natural language feedback for query refinement.
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Sep 02, 2020
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