Datasets:
File size: 3,398 Bytes
323f2d4 c321444 4a68a6a c321444 1048cd7 481f76c c321444 52c9efa 5391641 76727a2 5391641 52c9efa c321444 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
---
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: labels of test set are unknown.
## Source
Please refer to [github repo](https://github.com/tzshi/squall/) for source data.
## Use
```python
from datasets import load_dataset
dataset = load_dataset("siyue/squall","0")
```
Example:
```python
{
'nt': 'nt-10922',
'tbl': '204_879',
'columns':
{
'raw_header': ['year', 'host / location', 'division i overall', 'division i undergraduate', 'division ii overall', 'division ii community college'],
'tokenized_header': [['year'], ['host', '\\\\/', 'location'], ['division', 'i', 'overall'], ['division', 'i', 'undergraduate'], ['division', 'ii', 'overall'], ['division', 'ii', 'community', 'college']],
'column_suffixes': [['number'], ['address'], [], [], [], []],
'column_dtype': ['number', 'address', 'text', 'text', 'text', 'text'],
'example': ['1997', 'penn', 'chicago', 'swarthmore', 'harvard', 'valencia cc']
},
'nl': ['when', 'was', 'the', 'last', 'time', 'the', 'event', 'was', 'held', 'in', 'minnesota', '?'],
'nl_pos': ['WRB', 'VBD-AUX', 'DT', 'JJ', 'NN', 'DT', 'NN', 'VBD-AUX', 'VBN', 'IN', 'NNP', '.'],
'nl_ner': ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'LOCATION', 'O'],
'nl_incolumns': [False, False, False, False, False, False, False, False, False, False, False, False],
'nl_incells': [False, False, False, False, False, False, False, False, False, False, True, False],
'columns_innl': [False, False, False, False, False, False],
'tgt': '2007',
'sql':
{
'sql_type': ['Keyword', 'Column', 'Keyword', 'Keyword', 'Keyword', 'Column', 'Keyword', 'Literal.String', 'Keyword', 'Keyword', 'Column', 'Keyword', 'Keyword', 'Keyword'],
'value': ['select', 'c1', 'from', 'w', 'where', 'c2', '=', "'minnesota'", 'order', 'by', 'c1_number', 'desc', 'limit', '1'],
'span_indices': [[], [], [], [], [], [], [], [10, 10], [], [], [], [], [], []]
},
'nl_ralign':
{
'aligned_sql_token_type': ['None', 'None', 'Column', 'Column', 'Column', 'None', 'None', 'None', 'Column', 'Column', 'Literal', 'None'],
'aligned_sql_token_info': [None, None, 'c1_number', 'c1_number', 'c1', None, None, None, 'c2', 'c2', None, None],
'align':
{
'nl_indices': [[10], [9, 8], [4], [3, 2]],
'sql_indices': [[7], [5], [1], [8, 9, 10, 11, 12, 13]]
}
},
'align':
{
'nl_indices': [[10], [9, 8], [4], [3, 2]],
'sql_indices': [[7], [5], [1], [8, 9, 10, 11, 12, 13]]
}
}
```
## 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},
}
``` |