WikiSQL

Overview edit

Variants: WikiSQL

Associated Benchmarks

This dataset is used in 4 benchmarks:

• Question Answering - Metrics: Exact Match (EM)
• Code Generation - Metrics: Execution Accuracy, Exact Match Accuracy
• Semantic Parsing - Metrics: Accuracy, Denotation accuracy (test)
• SQL-to-Text - Metrics: BLEU-4

Recent Benchmark Submissions

Research Papers

Recent papers with results on this dataset:

• Piece of Table: A Divide-and-Conquer Approach for Selecting Sub-Tables in Table Question Answering (2024) -
• TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition (2024) -
• CABINET: Content Relevance based Noise Reduction for Table Question Answering (2024) -
• ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022) -
• TAPEX: Table Pre-training via Learning a Neural SQL Executor (2021) -
• TAPAS: Weakly Supervised Table Parsing via Pre-training (2020) -
• Content Enhanced BERT-based Text-to-SQL Generation (2019) -
• TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation (2018) -
• TypeSQL: Knowledge-based Type-Aware Neural Text-to-SQL Generation (2018) -
• Semantic Parsing with Syntax- and Table-Aware SQL Generation (2018) -
• Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks (2018) -
• Natural Language to Structured Query Generation via Meta-Learning (2018) -
• Bidirectional Attention for SQL Generation (2017) -
• Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (2017) -
• Gated Graph Sequence Neural Networks (2015) -