Weakly Supervised Text-to-SQL Parsing through Question Decomposition
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| _version_ | 1866913455441707008 |
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| author | Wolfson, Tomer Deutch, Daniel Berant, Jonathan |
| author_facet | Wolfson, Tomer Deutch, Daniel Berant, Jonathan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_06311 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Weakly Supervised Text-to-SQL Parsing through Question Decomposition Wolfson, Tomer Deutch, Daniel Berant, Jonathan Computation and Language Artificial Intelligence Databases 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. |
| title | Weakly Supervised Text-to-SQL Parsing through Question Decomposition |
| topic | Computation and Language Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2112.06311 |