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DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks

Summary: DBTagger reframes NLIDB keyword mapping as a sequence tagging problem, using Bi-Directional RNNs with POS features in a multi-task learning setup. End-to-end, schema-independent NLQ-to-SQL tagging attains 92.4% average accuracy across eight datasets and scales to large schemas with speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12596
Venue
VLDB
Year
2021
Pagerank
4.1945683e-05
Overall Rank
11,540 | 19.72%
DOI
10.14778/3446095.3446103

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