{
 "id": "deepmind/narrativeqa",
 "owner": "deepmind",
 "name": "narrativeqa",
 "downloads": 7304,
 "likes": 70,
 "last_modified": "2024-03-06T07:33:05.000Z",
 "stated_licence": "apache-2.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [
  "text2text-generation"
 ],
 "citation": "@article{kocisky-etal-2018-narrativeqa,\n    title = \"The {N}arrative{QA} Reading Comprehension Challenge\",\n    author = \"Ko{\\v{c}}isk{\\'y}, Tom{\\'a}{\\v{s}}  and\n      Schwarz, Jonathan  and\n      Blunsom, Phil  and\n      Dyer, Chris  and\n      Hermann, Karl Moritz  and\n      Melis, G{\\'a}bor  and\n      Grefenstette, Edward\",\n    editor = \"Lee, Lillian  and\n      Johnson, Mark  and\n      Toutanova, Kristina  and\n      Roark, Brian\",\n    journal = \"Transactions of the Association for Computational Linguistics\",\n    volume = \"6\",\n    year = \"2018\",\n    address = \"Cambridge, MA\",\n    publisher = \"MIT Press\",\n    url = \"https://aclanthology.org/Q18-1023\",\n    doi = \"10.1162/tacl_a_00023\",\n    pages = \"317--328\",\n    abstract = \"Reading comprehension (RC){---}in contrast to information retrieval{---}requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie script",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive."
}