mlsanityData to 14 Oct 2026

AskYourDB: An end-to-end system for querying and visualizing relational databases using natural language

arXiv 2210.08532 ↗First posted 16 Oct 2022cs.DB cs.CL cs.IRcited by 1

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Abstract (arXiv)

Querying databases for the right information is a time consuming and error-prone task and often requires experienced professionals for the job. Furthermore, the user needs to have some prior knowledge about the database. There have been various efforts to develop an intelligence which can help business users to query databases directly. However, there has been some successes, but very little in terms of testing and deploying those for real world users. In this paper, we propose a semantic parsing approach to address the challenge of converting complex natural language into SQL and institute a product out of it. For this purpose, we modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production. To make the product serviceable to businesses we added an automatic visualization framework over the queried results.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 16 Oct 2022.

Builds on

16 of 41 references.

References · 16

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  5. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
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  10. 2019Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representationcited by 210
  11. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  12. 2020Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Trainingcited by 65
4 more
  1. 2021SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQLcited by 43
  2. 2019Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statementscited by 31
  3. 2019Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timelinecited by 20
  4. 2020TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generationcited by 12

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Citing papers per year: 2022: 1.

1 paper.

  1. 2022On the Security Vulnerabilities of Text-to-SQL Modelscited by 3