|
--- |
|
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. |
|
|
|
## Code |
|
Please refer to our [github repo](https://github.com/tzshi/squall/) for code implementation. |
|
|
|
## 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}, |
|
} |
|
``` |
