Weakly Supervised Text-to-SQL Parsing through Question Decomposition (2022.findings-naacl)
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| Challenge: | Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. |
| Approach: | They propose a weak supervision approach for training text-to-SQL parsers by using a question meaning representation called QDMR to synthesize SQL queries from annotated NL-SqL data. |
| Outcome: | The proposed model performs competitively with those trained on annotated NL-SQL data. |
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