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2021

End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task

Shi, Peng, Yu, Tao, Ng, Patrick et al.

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In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling.

To encourage the model to learn better encoding ability, we propose a column selection auxiliary task to empower the encoder with the relevance matching capability by using explicit learning targets.
Furthermore, we propose two value filling methods to build the bridge from the existing zero-shot semantic parsers to real-world applications, considering most of the existing parsers ignore the values filling in the synthesized SQL.
With experiments on Spider, our proposed framework improves over the baselines on the execution accuracy and exact set match accuracy when database contents are unavailable, and detailed analysis sheds light on future work.

End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task · Around

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Representing schema structure with graph neural networks for text-to-sql parsing
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Ben Bogin, Matt Gardner, and Jonathan Berant. 2019c · 1905
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Towards complex text-to-sql in cross-domain database with intermediate representation
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Jiaqi Guo, Zecheng Zhan, Yan Gao, Yan Xiao, Jian-Guang Lou, Ting Liu, and Dongmei Zhang. 2019 · 1905
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
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Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2019 · 1911
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