{"url":"/dataset/movieqa","name":"MovieQA","full_name":"MovieQA","description_markdown":"The **MovieQA** dataset is a dataset for movie question answering. to evaluate automatic story comprehension from both video and text. The data set consists of almost 15,000 multiple choice question answers obtained from over 400 movies and features high semantic diversity. Each question comes with a set of five highly plausible answers; only one of which is correct. The questions can be answered using multiple sources of information: movie clips, plots, subtitles, and for a subset scripts and DVS.\r\n\r\nSource: [Movie Question Answering: Remembering the Textual Cues for Layered Visual Contents](https://arxiv.org/abs/1804.09412)\r\nImage Source: [https://www.researchgate.net/figure/Examples-of-multiple-choice-QA-from-the-MovieQA-dataset-Each-question-has-5_fig2_321379716](https://www.researchgate.net/figure/Examples-of-multiple-choice-QA-from-the-MovieQA-dataset-Each-question-has-5_fig2_321379716)","description_withheld":null,"homepage":"http://movieqa.cs.toronto.edu/home/","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/movieqa-understanding-stories-in-movies","title":"MovieQA: Understanding Stories in Movies through Question-Answering","first_author":"Makarand Tapaswi","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Video Question Answering","url":"/task/video-question-answering","datasets_with_task":"/datasets/task/video-question-answering"},{"name":"Video Story QA","url":"/task/video-story-qa","datasets_with_task":"/datasets/task/video-story-qa"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MovieQA"],"data_loaders":[{"repo":"https://github.com/thanhdat77/videoquestionsansweringdataset","url":"https://github.com/thanhdat77/videoquestionsansweringdataset","frameworks":["tf"]}],"num_papers_in_archive":86,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-story-qa-on-movieqa","task":"Video Story QA","dataset_variant":"MovieQA","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Long Story Short","paper":"/paper/long-story-short-a-summarize-then-search","metrics":{"Accuracy":"51.49"},"code_links":[{"title":"JiwanChung/long-story-short","url":"https://github.com/JiwanChung/long-story-short"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/long-story-short-a-summarize-then-search","title":"Long Story Short: a Summarize-then-Search Method for Long Video Question Answering","date":"2023-11-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/progressive-attention-memory-network-for","title":"Progressive Attention Memory Network for Movie Story Question Answering","date":"2019-04-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-read-write-memory-network-for-movie-story","title":"A Read-Write Memory Network for Movie Story Understanding","date":"2017-09-27","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}