Datasets:
metadata
license: mit
task_categories:
- table-question-answering
language:
- en
pretty_name: SQUALL
size_categories:
- 10K<n<100K
SQUALL Dataset
To explore the utility of fine-grained, lexical-level supervision, authors introduce SQUALL, a dataset that enriches 11,276 WikiTableQuestions English-language questions with manually created SQL equivalents plus alignments between SQL and question fragments. 5-fold splits are applied to the full dataset (1 fold as dev set at each time). The subset defines which fold is selected as the validation dataset.
WARN: alignment data (i.e., nl_ralign and align) is not implemented.
Source
Please refer to github repo for source data.
Contact
For any issues or questions, kindly email us at: Siyue Zhang (siyue001@e.ntu.edu.sg).
Citation
@inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020,
Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries},
Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee},
Booktitle = {Findings of EMNLP},
Year = {2020},
}