{"url":"/dataset/quac","name":"QuAC","full_name":"Question Answering in Context","description_markdown":"Question Answering in Context is a large-scale dataset that consists of around 14K crowdsourced Question Answering dialogs with 98K question-answer pairs in total. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans) from the text.\r\n\r\nSource: [https://paperswithcode.com/paper/quac-question-answering-in-context-1/](https://paperswithcode.com/paper/quac-question-answering-in-context-1/)\r\nImage Source: [https://paperswithcode.com/paper/quac-question-answering-in-context-1/](https://paperswithcode.com/paper/quac-question-answering-in-context-1/)","description_withheld":null,"homepage":"https://quac.ai/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/quac-question-answering-in-context-1","title":"QuAC: Question Answering in Context","first_author":"Eunsol Choi","url":null},"license":{"name":"CC BY-SA 4.0","url":"http://creativecommons.org/licenses/by-sa/4.0/legalcode"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Reading Comprehension","url":"/task/reading-comprehension","datasets_with_task":"/datasets/task/reading-comprehension"},{"name":"Conversational Question Answering","url":"/task/conversational-question-answering","datasets_with_task":"/datasets/task/conversational-question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["QuAC"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/quac","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/allenai/quac","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/facebookresearch/ParlAI","url":"https://parl.ai/docs/tasks.html#question-answering-in-context","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/quac-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/quac","frameworks":["tf","jax"]},{"repo":"https://github.com/allenai/allennlp-models","url":"https://docs.allennlp.org/models/main/models/rc/dataset_readers/quac/","frameworks":["pytorch"]}],"num_papers_in_archive":178,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-quac","task":"Question Answering","dataset_variant":"QuAC","rows":2,"metrics":["F1","HEQD","HEQQ"],"first_row_in_archive_order":{"model":"FlowQA (single model)","paper":"/paper/flowqa-grasping-flow-in-history-for","metrics":{"F1":"64.1","HEQD":"5.8","HEQQ":"59.6"},"code_links":[{"title":"momohuang/FlowQA","url":"https://github.com/momohuang/FlowQA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","rows_on_this_dataset":1,"code_links":67,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":65,"samples_ran":15,"samples_unverified":50,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flowqa-grasping-flow-in-history-for","title":"FlowQA: Grasping Flow in History for Conversational Machine Comprehension","date":"2018-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":65,"samples_ran":15,"samples_unverified":50,"pointer_only_for_licence":4,"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."}