Weakly Supervised Text-to-SQL Parsing through Question Decomposition

• URL: http://arxiv.org/abs/2112.06311v4
• Date: Fri, 2 Aug 2024 14:21:43 GMT
• Title: Weakly Supervised Text-to-SQL Parsing through Question Decomposition
• Authors: Tomer Wolfson, Daniel Deutch, Jonathan Berant,
• Abstract summary: We take advantage of the recently proposed question meaning representation called QDMR. Given questions, their QDMR structures (annotated by non-experts or automatically predicted) and the answers, we are able to automatically synthesizesql queries. Our results show that the weakly supervised models perform competitively with those trained on NL- benchmark data.
• Score: 53.22128541030441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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