What does this research mean for the field?

A transformer-based Natural Language Interface to Databases (NLIDB) utilizing a BERT-base tokenizer achieves up to 69% accuracy in translating natural language to SQL, performing well on simple queries but struggling with complex inter-table relationships. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to develop and evaluate a natural language interface to databases that translates queries into SQL.

April 8, 2026Open Access

Implementation of transformer model for natural language to SQL query translation

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Authors

KYKateryna YalovaUniversity of Information Technology and Management in Rzeszow MBMykhailo BabenkoDniprovsk State Technical University KYKseniia YashynaSIRIS Academic

Discussion

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Implication

This experimental evaluation demonstrates effective SQL query generation from natural language using a transformer model.

Key Points

The aim is to develop and evaluate a natural language interface to databases that translates queries into SQL.
Developed a transformer-based architecture for SQL query generation.
Conducted input query preprocessing, including tokenization and embedding.
Utilized the Spider corpus for training and evaluation.
Employed BLEU metric for measuring accuracy and lexical similarity.
Achieved a maximum accuracy of 69% with BERT-base tokenizer.
Obtained a BLEU score of 46% using the BERT-base tokenizer.
Best performance observed on simple and short queries.

Cite This Study

Yalova et al. (2025) studied this question.

synapsesocial.com/papers/69d5f14b74eaea4b11a7ad4b https://doi.org/10.5281/zenodo.19439051

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Implementation of transformer model for natural language to SQL query translation

Discussion

Member takes

Implication

Key Points

Cite This Study

Implementation of transformer model for natural language to SQL query translation

Discussion

Member takes

Implication

Key Points

Cite This Study

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