{
 "id": "allenai/quac",
 "owner": "allenai",
 "name": "quac",
 "downloads": 1465,
 "likes": 39,
 "last_modified": "2024-01-18T11:14:05.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [
  {
   "id": "wikipedia",
   "licence": "cc-by-sa-3.0",
   "found": true,
   "evidence": "https://huggingface.co/datasets/legacy-datasets/wikipedia/blob/97a0b052c326b45fb68593a14972d9eed884cd17/README.md"
  }
 ],
 "gated": false,
 "tasks": [
  "question-answering",
  "text-generation",
  "fill-mask"
 ],
 "citation": "@inproceedings{choi-etal-2018-quac,\n    title = \"{Q}u{AC}: Question Answering in Context\",\n    author = \"Choi, Eunsol  and\n      He, He  and\n      Iyyer, Mohit  and\n      Yatskar, Mark  and\n      Yih, Wen-tau  and\n      Choi, Yejin  and\n      Liang, Percy  and\n      Zettlemoyer, Luke\",\n    booktitle = \"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct # \"-\" # nov,\n    year = \"2018\",\n    address = \"Brussels, Belgium\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D18-1241\",\n    doi = \"10.18653/v1/D18-1241\",\n    pages = \"2174--2184\",\n    abstract = \"We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve 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 from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting ",
 "signal": "conflict",
 "reason": "The declared licence (mit) may conflict with a source dataset's reported share-alike licence (cc-by-sa-3.0).",
 "revision": "2c1b73f07a629a338127b6a9761c64bf3e969179",
 "stated_evidence": "https://huggingface.co/datasets/allenai/quac/blob/2c1b73f07a629a338127b6a9761c64bf3e969179/README.md",
 "file_evidence": "https://huggingface.co/datasets/allenai/quac/tree/2c1b73f07a629a338127b6a9761c64bf3e969179",
 "card_nc": false
}