Deep Learning Approaches for Text-to-SQL Systems

Presented at the 2021 International Conference on Extending Database Technology (EDBT)

Abstract

To bridge the gap between users and data, numerous text-to-SQL systems have been developed that allow users to pose natural language questions over relational databases. Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this tutorial is to present recent advances of deep learning techniques for text-to-SQL translation, and to highlight open problems and new research opportunities for researchers and practitioners in the fields of database systems, natural language processing and deep learning.

Outline

  1. The Text-to-SQL Problem
  2. Text-to-SQL Landscape
  3. Available Benchmarks
  4. Natural Language Representation
  5. Text-to-SQL Deep Learning Approaches
  6. Key Text-to-SQL Systems
  7. Challenges & Research Opportunities

Video Teaser

Presenters

George Katsogiannis

Georgia Koutrika

Material

Feel free to download the slides of the tutorial here.