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)
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Authors
- 1. Arif Usta
- 2. Akifhan Karakayali
- 3. Özgür Ulusoy
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 206 | Constructing an Interactive Natural Language Interface for Relational Databases | 2015 | VLDB | 0.00034667032 |
| 535 | ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores | 2016 | VLDB | 0.00020727678 |
| 984 | Natural language to SQL: Where are we today? | 2020 | VLDB | 0.00014857465 |
| 1,168 | SODA: Generating SQL for Business Users | 2012 | VLDB | 0.00013541143 |
| 2,321 | DBPal: A Fully Pluggable NL2SQL Training Pipeline | 2020 | SIGMOD | 9.03609e-05 |
| 2,816 | DBPal: A Learned NL-Interface for Databases | 2018 | SIGMOD | 8.0694738e-05 |
| 5,281 | State of the Art and Open Challenges in Natural Language Interfaces to Data | 2020 | SIGMOD | 5.5896272e-05 |
| 5,455 | Natural Language Data Management and Interfaces: Recent Development and Open Challenges | 2017 | SIGMOD | 5.4977219e-05 |
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