mlsanityData to 7 Oct 2026

An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network

arXiv 2406.15936 ↗First posted 22 Jun 2024cs.CY cs.AI cs.DBcited by 0

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Abstract (arXiv)

Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an attempt to mitigate these challenges, but those systems have their own limitations. This paper describes our novel approach to automating the process of grading SQL queries. Unlike previous approaches, we employ a unique convolutional neural network architecture that employs a parameter-sharing approach for different machine learning tasks that enables the architecture to induce different knowledge representations of the data to increase its potential for understanding SQL statements.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 22 Jun 2024.

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References · 9

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  3. 2018How Does Batch Normalization Help Optimization?cited by 355
  4. 2019Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsingcited by 74
  5. 2017Variants of RMSProp and Adagrad with Logarithmic Regret Boundscited by 50
  6. 2021ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parsercited by 35
  7. 2018Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queriescited by 11
  8. 2019Edit Based Grading of SQL Queriescited by 1
  9. 2020Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clusteringcited by 1

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