testzer0/AmbiQT

Code and Assets for "Benchmarking and Improving Text-to-SQL Generation Under Ambiguity" (EMNLP 2023)

29
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Experimental

When you ask a database a question in plain English, it sometimes has multiple valid interpretations, but most systems only give you one. This project provides a new way to get all the plausible database queries from an ambiguous English question, so you don't miss important insights. It takes an English question as input and provides multiple SQL queries as output, making it useful for data analysts or business users exploring data.

No commits in the last 6 months.

Use this if you need to ensure your natural language queries to a database capture all possible interpretations, especially when dealing with ambiguous phrasing or similar-sounding data labels.

Not ideal if your queries are always clear-cut with only one possible SQL translation, or if you primarily work directly with SQL and do not use natural language interfaces.

data-analysis business-intelligence database-querying natural-language-processing data-exploration
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 8 / 25

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Stars

9

Forks

1

Language

Python

License

MIT

Last pushed

Oct 15, 2023

Commits (30d)

0

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