{"url":"/dataset/ott-qa","name":"OTT-QA","full_name":null,"description_markdown":"The Open Table-and-Text Question Answering (**OTT-QA**) dataset contains open questions which require retrieving tables and text from the web to answer. This dataset is re-annotated from the previous HybridQA dataset. The dataset is collected by UCSB NLP group and issued under MIT license.\n\nSource: [https://github.com/wenhuchen/OTT-QA](https://github.com/wenhuchen/OTT-QA)\nImage Source: [https://github.com/wenhuchen/OTT-QA](https://github.com/wenhuchen/OTT-QA)","description_withheld":null,"homepage":"https://github.com/wenhuchen/OTT-QA","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/open-question-answering-over-tables-and-text-1","title":"Open Question Answering over Tables and Text","first_author":"Wenhu Chen","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"}],"languages":[],"variants":["OTT-QA"],"data_loaders":[{"repo":"https://github.com/wenhuchen/OTT-QA","url":"https://github.com/wenhuchen/OTT-QA","frameworks":["pytorch"]}],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-ott-qa","task":"Question Answering","dataset_variant":"OTT-QA","rows":3,"metrics":["ANS-EM"],"first_row_in_archive_order":{"model":"Fusion Retriever+ETC","paper":"/paper/open-question-answering-over-tables-and-text-1","metrics":{"ANS-EM":"27.2"},"code_links":[{"title":"wenhuchen/OTT-QA","url":"https://github.com/wenhuchen/OTT-QA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/denoising-table-text-retrieval-for-open","title":"Denoising Table-Text Retrieval for Open-Domain Question Answering","date":"2024-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/reasoning-over-hybrid-chain-for-table-and-1","title":"Reasoning over Hybrid Chain for Table-and-Text Open Domain QA","date":"2022-01-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/open-question-answering-over-tables-and-text-1","title":"Open Question Answering over Tables and Text","date":"2020-10-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":3,"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":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":0,"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."}