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Improving Text-to-SQL with a Hybrid Decoding Method.

Geunyeong Jeong¹, Mirae Han¹, Seulgi Kim²

¹Department of Artificial Intelligence, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.

Entropy (Basel, Switzerland)

|March 29, 2023

Summary

This summary is machine-generated.

A new Hybrid decoder for text-to-SQL models improves performance and accuracy by combining sketch-based and generation-based decoding methods. This approach enhances SQL query generation by considering element relevance and simplifying value prediction.

Keywords:

natural language processing pointer network semantic parsing text-to-SQL

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Area of Science:

Natural Language Processing
Database Management
Artificial Intelligence

Background:

Text-to-SQL models convert natural language questions into SQL queries.
Existing sketch-based and generation-based decoding methods have limitations in performance and accuracy.
Sketch-based methods lack SQL element relevance, while generation-based methods can be slow and error-prone.

Purpose of the Study:

To propose a novel Hybrid decoder for text-to-SQL models.
To address the shortcomings of existing decoding methods.
To improve the accuracy and efficiency of SQL query generation.

Main Methods:

Developed a Hybrid decoder combining sketch-based and generation-based approaches.
Incorporated inter-SQL element information and defined generatable elements.
Introduced a Value prediction module for WHERE clause values.
Designed a new evaluation measure for syntactic correctness.

Main Results:

The Hybrid decoder improves performance by leveraging mutual information among SQL elements.
It enhances efficiency by simplifying the decoding process compared to generation-based methods.
The proposed model consistently generates syntactically accurate SQL queries.

Conclusions:

The Hybrid decoder offers a significant advancement in text-to-SQL technology.
It effectively balances performance, accuracy, and efficiency in SQL query generation.
The novel evaluation measure confirms the syntactic correctness of generated queries.