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An interaction-modeling mechanism for context-dependent Text-to-SQL translation based on heterogeneous graph

Wei Yu1, Tao Chang1, Xiaoting Guo1

  • 1College of Computer, National University of Defense Technology, Kaifu District, Changsha 410073, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 3, 2021
PubMed
Summary

This study introduces an interaction-modeling mechanism for context-dependent Text-to-SQL tasks, improving SQL query generation by encoding historical interactions and database schemas. The new approach represents texts as graphs for better integration and performance.

Keywords:
Context-dependent Text-to-SQLHeterogeneous graph aggregationInteraction modeling

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Area of Science:

  • Natural Language Processing
  • Database Management
  • Artificial Intelligence

Background:

  • Context-dependent Text-to-SQL requires integrating historical interactions and database schemas.
  • Previous methods using Recurrent Neural Networks (RNNs) struggle to model intrinsic text relationships effectively.

Purpose of the Study:

  • To propose an interaction-modeling mechanism for enhanced Text-to-SQL query generation.
  • To effectively encode and integrate diverse text types including question sentences, SQL queries, and database schemas.

Main Methods:

  • Representing different text types as individual graphs.
  • Utilizing heterogeneous graph aggregation to capture interactions and create a holistic representation.
  • Generating SQL queries based on current questions and aggregated information.

Main Results:

  • The proposed model demonstrates competitive performance on the SparC and CoSQL datasets.
  • The interaction-modeling mechanism effectively captures and integrates contextual information.
  • Achieved a favorable balance between performance and space-time complexity.

Conclusions:

  • The interaction-modeling mechanism offers a superior approach to Text-to-SQL compared to traditional RNN-based methods.
  • This graph-based approach enhances the accuracy and efficiency of SQL query generation in multi-turn scenarios.
  • The model's effectiveness is validated on established benchmark datasets.