{
 "id": "SEACrowd/maxm",
 "owner": "SEACrowd",
 "name": "maxm",
 "downloads": 25,
 "likes": 0,
 "last_modified": "2024-06-24T13:22:46.000Z",
 "stated_licence": "other",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [
  "LICENSE"
 ],
 "sources": [],
 "gated": false,
 "tasks": [
  "visual-question-answering"
 ],
 "citation": "@inproceedings{changpinyo-etal-2023-maxm,\n    title = \"{M}a{XM}: Towards Multilingual Visual Question Answering\",\n    author = \"Changpinyo, Soravit  and\n      Xue, Linting  and\n      Yarom, Michal  and\n      Thapliyal, Ashish  and\n      Szpektor, Idan  and\n      Amelot, Julien  and\n      Chen, Xi  and\n      Soricut, Radu\",\n    editor = \"Bouamor, Houda  and\n      Pino, Juan  and\n      Bali, Kalika\",\n    booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2023\",\n    month = dec,\n    year = \"2023\",\n    address = \"Singapore\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2023.findings-emnlp.176\",\n    doi = \"10.18653/v1/2023.findings-emnlp.176\",\n    pages = \"2667--2682\",\n    abstract = \"Visual Question Answering (VQA) has been primarily studied\n    through the lens of the English language. Yet, tackling VQA in other\n    languages in the same manner would require a considerable amount of\n    resources. In this paper, we propose scalable solutions to multilingual\n    visual question answering (mVQA), on both data and modeling fronts. We first\n    propose a translation-based framework to mVQA data generation that requires\n    much less human annotation efforts than the conventional approach of\n    directly collection questions and answers. Then, we apply our framework to\n    the multilingual captions in the Crossmodal-3600 dataset and develop an\n    efficient annotation protocol to create MaXM, a test-only V",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "01ff41027b1e2e6f8c58223f984063a6dd43aacd",
 "stated_evidence": "https://huggingface.co/datasets/SEACrowd/maxm/blob/01ff41027b1e2e6f8c58223f984063a6dd43aacd/README.md",
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 "card_nc": false
}