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Publication type: Conference paper
Type of review: Peer review (publication)
Title: ValueNet : a natural language-to-SQL system that learns from database information
Authors: Brunner, Ursin
Stockinger, Kurt
et. al: No
DOI: 10.21256/zhaw-22000
Proceedings: Proceedings of the 37th ICDE
Conference details: International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021
Issue Date: Apr-2021
Publisher / Ed. Institution: IEEE
Other identifiers: arXiv:2006.00888v2
Language: English
Subjects: NL-to-SQL; Natural language interface; Neural network; Transformer
Subject (DDC): 005: Computer programming, programs and data
Abstract: In this paper we propose ValueNet light and ValueNet - two end-to-end Natural Language-to-SQL systems that incorporate values using the challenging Spider dataset.The main idea of our approach is to use not only metadata information from the underlying database but also information on the base data as input for our neural network architecture. In particular, we propose a novel architecture sketch to extract values from a user question and come up with possible value candidates which are not explicitly mentioned in the question. We then use a neural model based on an encoder-decoder architecture to synthesize the SQL query. Finally, we evaluate our model on the Spider challenge using the Execution Accuracy metric, a more difficult metric than used by most participants of the challenge. Our experimental evaluation demonstrates that ValueNet light and ValueNet reach state-of-the-art results of 67% and 62% accuracy, respectively, for translating from NL to SQL whilst incorporating values.
Fulltext version: Updated version
License (according to publishing contract): Not specified
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: INODE - Intelligent Open Data Exploration
Appears in collections:Publikationen School of Engineering

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