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Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach

Summary: Solo enables natural-language data discovery with self-supervised training, no labeled data needed. It develops self-supervised data generation, table representations, and relevance models for end-to-end learned discovery that outperforms baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6825
Venue
SIGMOD
Year
2023
Pagerank
5.6679948e-05
Overall Rank
7,220 | 50.47%
DOI
10.1145/3626756

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod23,
        title = {{Solo: Data Discovery Using Natural Language Questions Via A Self-Supervised Approach}},
        author = {Wang, Qiming and Fernandez, Raul Castro},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626756},
        url = {https://dl.acm.org/doi/10.1145/3626756},
        year = {2023}
}

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