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Deep Learning Approaches for Text-to-SQL systems

ATHENA, Greece
George Katsogiannis
ATHENA, Greece
Georgia Koutrika
accepted in:
EDBT, 24th International Conference on Extending Database Technology, 2021
Yet to be published
March 1, 2021
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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.

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