README.md

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: labels of test set are unknown.

Source

Please refer to github repo for source data.

Use

from datasets import load_dataset
dataset = load_dataset("siyue/squall","0")

Example:

{
    '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},
}