{"url":"/dataset/sqa","name":"SQA","full_name":"SequentialQA","description_markdown":"The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. It has 6,066 sequences with 17,553 questions in total.\r\n\r\nSource: [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253)","description_withheld":null,"homepage":"https://www.microsoft.com/en-us/download/details.aspx?id=54253","introduced_date":"2017-07-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/search-based-neural-structured-learning-for","title":"Search-based Neural Structured Learning for Sequential Question Answering","first_author":"Mohit Iyyer","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Semantic Parsing","url":"/task/semantic-parsing","datasets_with_task":"/datasets/task/semantic-parsing"}],"languages":[],"variants":["SQA"],"data_loaders":[],"num_papers_in_archive":37,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-parsing-on-sqa","task":"Semantic Parsing","dataset_variant":"SQA","rows":2,"metrics":["Denotation Accuracy","Accuracy"],"first_row_in_archive_order":{"model":"TAPEX-Large","paper":"/paper/tapex-table-pre-training-via-learning-a","metrics":{"Denotation Accuracy":"74.5"},"code_links":[{"title":"microsoft/Table-Pretraining","url":"https://github.com/microsoft/Table-Pretraining"},{"title":"sohanpatnaik106/cabinet_qa","url":"https://github.com/sohanpatnaik106/cabinet_qa"},{"title":"MindCode-4/code-5","url":"https://github.com/MindCode-4/code-5/tree/main/tapex"},{"title":"pwc-1/Paper-9","url":"https://github.com/pwc-1/Paper-9/tree/main/1/tapex"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/tapex-table-pre-training-via-learning-a","title":"TAPEX: Table Pre-training via Learning a Neural SQL Executor","date":"2021-07-16","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/tapas-weakly-supervised-table-parsing-via-pre","title":"TAPAS: Weakly Supervised Table Parsing via Pre-training","date":"2020-04-05","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":0,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":14,"samples_ran":0,"samples_unverified":14,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}