{"url":"/dataset/narrativeqa","name":"NarrativeQA","full_name":"NarrativeQA","description_markdown":"The NarrativeQA dataset includes a list of documents with Wikipedia summaries, links to full stories, and questions and answers.\r\n\r\nSource: [DeepMind](https://deepmind.com/research/open-source/narrativeqa)\r\nImage Source: [Kočiský et al ](https://arxiv.org/pdf/1712.07040v1.pdf)","description_withheld":null,"homepage":"https://deepmind.com/research/open-source/narrativeqa","introduced_date":"2017-12-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-narrativeqa-reading-comprehension","title":"The NarrativeQA Reading Comprehension Challenge","first_author":"Tomáš Kočiský","url":null},"license":{"name":"Apache-2.0 License","url":"https://github.com/deepmind/narrativeqa"},"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"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NarrativeQA"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/narrativeqa","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/narrativeqa_manual","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/deepmind/narrativeqa_manual","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/deepmind/narrativeqa","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/facebookresearch/ParlAI","url":"https://parl.ai/docs/tasks.html#narrativeqa","frameworks":["pytorch"]}],"num_papers_in_archive":206,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-narrativeqa","task":"Question Answering","dataset_variant":"NarrativeQA","rows":10,"metrics":["Rouge-L","BLEU-1","BLEU-4","METEOR"],"first_row_in_archive_order":{"model":"Masque (NarrativeQA + MS MARCO)","paper":"/paper/multi-style-generative-reading-comprehension","metrics":{"BLEU-1":"54.11","BLEU-4":"30.43","METEOR":"26.13","Rouge-L":"59.87"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/distilling-knowledge-from-reader-to-retriever-1","title":"Distilling Knowledge from Reader to Retriever for Question Answering","date":"2020-12-08","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-discrete-hard-em-approach-for-weakly","title":"A Discrete Hard EM Approach for Weakly Supervised Question Answering","date":"2019-09-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-style-generative-reading-comprehension","title":"Multi-style Generative Reading Comprehension","date":"2019-01-08","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/densely-connected-attention-propagation-for","title":"Densely Connected Attention Propagation for Reading Comprehension","date":"2018-11-10","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/multi-granular-sequence-encoding-via-dilated","title":"Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension","date":"2018-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cut-to-the-chase-a-context-zoom-in-network","title":"Cut to the Chase: A Context Zoom-in Network for Reading Comprehension","date":"2018-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/commonsense-for-generative-multi-hop-question","title":"Commonsense for Generative Multi-Hop Question Answering Tasks","date":"2018-09-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-narrativeqa-reading-comprehension","title":"The NarrativeQA Reading Comprehension Challenge","date":"2017-12-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/bidirectional-attention-flow-for-machine","title":"Bidirectional Attention Flow for Machine Comprehension","date":"2016-11-05","rows_on_this_dataset":1,"code_links":27,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":4,"samples_harvested":20,"samples_ran":10,"samples_unverified":10,"pointer_only_for_licence":9,"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."}