TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Papers/Text-to-SQL in the Wild: A Naturally-Occurring Dataset Bas...

Text-to-SQL in the Wild: A Naturally-Occurring Dataset Based on Stack Exchange Data

Moshe Hazoom, Vibhor Malik, Ben Bogin

2021-06-09ACL (NLP4Prog) 2021 8Semantic ParsingText-To-SQLNatural Language Understanding
PaperPDFCode(official)

Abstract

Most available semantic parsing datasets, comprising of pairs of natural utterances and logical forms, were collected solely for the purpose of training and evaluation of natural language understanding systems. As a result, they do not contain any of the richness and variety of natural-occurring utterances, where humans ask about data they need or are curious about. In this work, we release SEDE, a dataset with 12,023 pairs of utterances and SQL queries collected from real usage on the Stack Exchange website. We show that these pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset, propose an evaluation metric based on comparison of partial query clauses that is more suitable for real-world queries, and conduct experiments with strong baselines, showing a large gap between the performance on SEDE compared to other common datasets.

Results

TaskDatasetMetricValueModel
Semantic ParsingSEDEPCM-F1 (dev)48.2T5-Large
Semantic ParsingSEDEPCM-F1 (test)50.6T5-Large
Text-To-SQLSEDEPCM-F1 (dev)48.2T5-Large
Text-To-SQLSEDEPCM-F1 (test)50.6T5-Large

Related Papers

Vision Language Action Models in Robotic Manipulation: A Systematic Review2025-07-14CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation2025-07-08XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL2025-07-07A Survey on Vision-Language-Action Models for Autonomous Driving2025-06-30State and Memory is All You Need for Robust and Reliable AI Agents2025-06-30Where, What, Why: Towards Explainable Driver Attention Prediction2025-06-29skLEP: A Slovak General Language Understanding Benchmark2025-06-26SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models2025-06-25