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        <title>Weakly Supervised Text-to-SQL Parsing through Question Decomposition</title>
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        <namePart type="given">Tomer</namePart>
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            <namePart type="given">Marie-Catherine</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
            <namePart type="given">Vladimir</namePart>
            <namePart type="family">Meza Ruiz</namePart>
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    <abstract>Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we propose a weak supervision approach for training text-to-SQL parsers. We take advantage of the recently proposed question meaning representation called QDMR, an intermediate between NL and formal query languages. Given questions, their QDMR structures (annotated by non-experts or automatically predicted), and the answers, we are able to automatically synthesize SQL queries that are used to train text-to-SQL models. We test our approach by experimenting on five benchmark datasets. Our results show that the weakly supervised models perform competitively with those trained on annotated NL-SQL data. Overall, we effectively train text-to-SQL parsers, while using zero SQL annotations.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-naacl.193</identifier>
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%0 Conference Proceedings
%T Weakly Supervised Text-to-SQL Parsing through Question Decomposition
%A Wolfson, Tomer
%A Deutch, Daniel
%A Berant, Jonathan
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Findings of the Association for Computational Linguistics: NAACL 2022
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F wolfson-etal-2022-weakly
%X Text-to-SQL parsers are crucial in enabling non-experts to effortlessly query relational data. Training such parsers, by contrast, generally requires expertise in annotating natural language (NL) utterances with corresponding SQL queries. In this work, we propose a weak supervision approach for training text-to-SQL parsers. We take advantage of the recently proposed question meaning representation called QDMR, an intermediate between NL and formal query languages. Given questions, their QDMR structures (annotated by non-experts or automatically predicted), and the answers, we are able to automatically synthesize SQL queries that are used to train text-to-SQL models. We test our approach by experimenting on five benchmark datasets. Our results show that the weakly supervised models perform competitively with those trained on annotated NL-SQL data. Overall, we effectively train text-to-SQL parsers, while using zero SQL annotations.
%R 10.18653/v1/2022.findings-naacl.193
%U https://aclanthology.org/2022.findings-naacl.193
%U https://doi.org/10.18653/v1/2022.findings-naacl.193
%P 2528-2542

Markdown (Informal)

[Weakly Supervised Text-to-SQL Parsing through Question Decomposition](https://aclanthology.org/2022.findings-naacl.193) (Wolfson et al., Findings 2022)

• Weakly Supervised Text-to-SQL Parsing through Question Decomposition (Wolfson et al., Findings 2022)

ACL

• Tomer Wolfson, Daniel Deutch, and Jonathan Berant. 2022. Weakly Supervised Text-to-SQL Parsing through Question Decomposition. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 2528–2542, Seattle, United States. Association for Computational Linguistics.