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MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations

Summary: MT-Teql applies metamorphic, semantics-preserving transformations to utterances and schemas, benchmarking NLIDB robustness without reannotation. Testing nine models found 15,433 defects; using triggering variants for augmentation removed 46.5% of errors. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13119
Venue
VLDB
Year
2022
Pagerank
7.4918609e-05
Overall Rank
3,354 | 76.99%
DOI
10.14778/3494124.3494139

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ma_vldb22,
        title = {{MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations}},
        author = {Ma, Pingchuan and Wang, Shuai},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {3},
        pages = {569--582},
        doi = {10.14778/3494124.3494139},
        url = {https://doi.org/10.14778/3494124.3494139},
        year = {2022}
}

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