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--- |
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license: mit |
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task_categories: |
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- table-question-answering |
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language: |
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- en |
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pretty_name: SQUALL |
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size_categories: |
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- 10K<n<100K |
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--- |
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## SQUALL Dataset |
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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. |
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WARN: labels of test set are unknown. |
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## Source |
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Please refer to [github repo](https://github.com/tzshi/squall/) for source data. |
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## Use |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("siyue/squall","0") |
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``` |
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Example: |
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```python |
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{ |
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'nt': 'nt-10922', |
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'tbl': '204_879', |
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'columns': |
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{ |
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'raw_header': ['year', 'host / location', 'division i overall', 'division i undergraduate', 'division ii overall', 'division ii community college'], |
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'tokenized_header': [['year'], ['host', '\\\\/', 'location'], ['division', 'i', 'overall'], ['division', 'i', 'undergraduate'], ['division', 'ii', 'overall'], ['division', 'ii', 'community', 'college']], |
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'column_suffixes': [['number'], ['address'], [], [], [], []], |
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'column_dtype': ['number', 'address', 'text', 'text', 'text', 'text'], |
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'example': ['1997', 'penn', 'chicago', 'swarthmore', 'harvard', 'valencia cc'] |
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}, |
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'nl': ['when', 'was', 'the', 'last', 'time', 'the', 'event', 'was', 'held', 'in', 'minnesota', '?'], |
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'nl_pos': ['WRB', 'VBD-AUX', 'DT', 'JJ', 'NN', 'DT', 'NN', 'VBD-AUX', 'VBN', 'IN', 'NNP', '.'], |
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'nl_ner': ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'LOCATION', 'O'], |
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'nl_incolumns': [False, False, False, False, False, False, False, False, False, False, False, False], |
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'nl_incells': [False, False, False, False, False, False, False, False, False, False, True, False], |
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'columns_innl': [False, False, False, False, False, False], |
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'tgt': '2007', |
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'sql': |
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{ |
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'sql_type': ['Keyword', 'Column', 'Keyword', 'Keyword', 'Keyword', 'Column', 'Keyword', 'Literal.String', 'Keyword', 'Keyword', 'Column', 'Keyword', 'Keyword', 'Keyword'], |
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'value': ['select', 'c1', 'from', 'w', 'where', 'c2', '=', "'minnesota'", 'order', 'by', 'c1_number', 'desc', 'limit', '1'], |
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'span_indices': [[], [], [], [], [], [], [], [10, 10], [], [], [], [], [], []] |
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}, |
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'nl_ralign': |
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{ |
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'aligned_sql_token_type': ['None', 'None', 'Column', 'Column', 'Column', 'None', 'None', 'None', 'Column', 'Column', 'Literal', 'None'], |
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'aligned_sql_token_info': [None, None, 'c1_number', 'c1_number', 'c1', None, None, None, 'c2', 'c2', None, None], |
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'align': |
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{ |
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'nl_indices': [[10], [9, 8], [4], [3, 2]], |
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'sql_indices': [[7], [5], [1], [8, 9, 10, 11, 12, 13]] |
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} |
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}, |
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'align': |
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{ |
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'nl_indices': [[10], [9, 8], [4], [3, 2]], |
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'sql_indices': [[7], [5], [1], [8, 9, 10, 11, 12, 13]] |
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} |
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} |
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``` |
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## Contact |
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For any issues or questions, kindly email us at: Siyue Zhang (siyue001@e.ntu.edu.sg). |
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## Citation |
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``` |
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@inproceedings{Shi:Zhao:Boyd-Graber:Daume-III:Lee-2020, |
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Title = {On the Potential of Lexico-logical Alignments for Semantic Parsing to {SQL} Queries}, |
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Author = {Tianze Shi and Chen Zhao and Jordan Boyd-Graber and Hal {Daum\'{e} III} and Lillian Lee}, |
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Booktitle = {Findings of EMNLP}, |
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Year = {2020}, |
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} |
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``` |
