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A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems

Summary: DL-based text-to-SQL survey: neural NL representations, large training datasets, and state-of-the-art systems. Benchmarks, competitions, and open problems highlighted; a research roadmap for DL-driven data management in NL-to-SQL. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6294
Venue
SIGMOD
Year
2021
Pagerank
7.5047676e-05
Overall Rank
3,339 | 77.10%
DOI
10.1145/3448016.3457543

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{katsogiannismeimarakis_sigmod21,
        title = {{A Deep Dive into Deep Learning Approaches for Text-to-SQL Systems}},
        author = {Katsogiannis-Meimarakis, George and Koutrika, Georgia},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457543},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457543},
        year = {2021}
}

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