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        <title>Error Detection for Text-to-SQL Semantic Parsing</title>
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        <namePart type="given">Shijie</namePart>
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    <abstract>Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect. Specifically, modern text-to-SQL parsers based on deep learning are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. In this paper, we propose a parser-independent error detection model for text-to-SQL semantic parsing. Using a language model of code as its bedrock, we enhance our error detection model with graph neural networks that learn structural features of both natural language questions and SQL queries. We train our model on realistic parsing errors collected from a cross-domain setting, which leads to stronger generalization ability. Experiments with three strong text-to-SQL parsers featuring different decoding mechanisms show that our approach outperforms parser-dependent uncertainty metrics. Our model could also effectively improve the performance and usability of text-to-SQL semantic parsers regardless of their architectures.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.785</identifier>
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%0 Conference Proceedings
%T Error Detection for Text-to-SQL Semantic Parsing
%A Chen, Shijie
%A Chen, Ziru
%A Sun, Huan
%A Su, Yu
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-error
%X Despite remarkable progress in text-to-SQL semantic parsing in recent years, the performance of existing parsers is still far from perfect. Specifically, modern text-to-SQL parsers based on deep learning are often over-confident, thus casting doubt on their trustworthiness when deployed for real use. In this paper, we propose a parser-independent error detection model for text-to-SQL semantic parsing. Using a language model of code as its bedrock, we enhance our error detection model with graph neural networks that learn structural features of both natural language questions and SQL queries. We train our model on realistic parsing errors collected from a cross-domain setting, which leads to stronger generalization ability. Experiments with three strong text-to-SQL parsers featuring different decoding mechanisms show that our approach outperforms parser-dependent uncertainty metrics. Our model could also effectively improve the performance and usability of text-to-SQL semantic parsers regardless of their architectures.
%R 10.18653/v1/2023.findings-emnlp.785
%U https://aclanthology.org/2023.findings-emnlp.785
%U https://doi.org/10.18653/v1/2023.findings-emnlp.785
%P 11730-11743

Markdown (Informal)

[Error Detection for Text-to-SQL Semantic Parsing](https://aclanthology.org/2023.findings-emnlp.785) (Chen et al., Findings 2023)

• Error Detection for Text-to-SQL Semantic Parsing (Chen et al., Findings 2023)

ACL

• Shijie Chen, Ziru Chen, Huan Sun, and Yu Su. 2023. Error Detection for Text-to-SQL Semantic Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11730–11743, Singapore. Association for Computational Linguistics.