Text to SQL Query Conversion Using Deep Learning: A Comparative Analysis

8 Pages Posted: 7 Sep 2019

See all articles by Anisha

Anisha

Government Engineering College, Sreekrishnapuram, Department of Computer Science and Engineering, Students

Rafeeque

Government College of Engineering Kannur

Reena

Government College of Engineering Kannur

Date Written: August 15, 2019

Abstract

In relational databases, a significant amount of world’s knowledge is stored. The ability of users to retrieve facts from a database is limited due to a lack of understanding of query languages. Intersection of Natural Language Processing (NLP) and human-computer intersection is the Natural Language Interface (NLI) which provides humans to interact with computer through the use of natural language. Here, NLI applied to relational databases translating Natural Language Queries to Structured Query Language (SQL). The proposed system is a NLP based system for converting natural language queries to database queries using Deep Neural Network. Spider, a new large-scale, cross-domain semantic parsing and text-to-SQL data set annotated by 11 college students released on November 2018 is used here. The system uses a deep learning framework such as basic sequence to sequence (Encoder-Decoder Model) and sequence to sequence plus attention. The model takes word embeddings of the query as the input and then sequence to sequence modeling is applied. The system is evaluated on the two models based on the exact matching and hardness criteria of questions. The evaluation results on Spider dataset shows that the proposed two deep learning model improves the results of text-to-SQL task. Spider dataset poses a major challenge for future research.

Keywords: Deep Learning; Natural Language Interface (NLI); Natural Language Processing (NLP); Semantic Parsing; Seq2Seq; Seq2Seq+Attention

Suggested Citation

T S, Anisha and P C, Rafeeque and Murali, Reena, Text to SQL Query Conversion Using Deep Learning: A Comparative Analysis (August 15, 2019). In proceedings of the International Conference on Systems, Energy & Environment (ICSEE) 2019, GCE Kannur, Kerala, July 2019. Available at SSRN: https://ssrn.com/abstract=3437616 or http://dx.doi.org/10.2139/ssrn.3437616

Anisha T S (Contact Author)

Government Engineering College, Sreekrishnapuram, Department of Computer Science and Engineering, Students ( email )

India

Rafeeque P C

Government College of Engineering Kannur ( email )

Kannur
India

Reena Murali

Government College of Engineering Kannur ( email )

Kannur
India

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