SEACrowd/maxm

Tasks

• visual-question-answering

Citation text from the card

@inproceedings{changpinyo-etal-2023-maxm,
    title = "{M}a{XM}: Towards Multilingual Visual Question Answering",
    author = "Changpinyo, Soravit  and
      Xue, Linting  and
      Yarom, Michal  and
      Thapliyal, Ashish  and
      Szpektor, Idan  and
      Amelot, Julien  and
      Chen, Xi  and
      Soricut, Radu",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.176",
    doi = "10.18653/v1/2023.findings-emnlp.176",
    pages = "2667--2682",
    abstract = "Visual Question Answering (VQA) has been primarily studied
    through the lens of the English language. Yet, tackling VQA in other
    languages in the same manner would require a considerable amount of
    resources. In this paper, we propose scalable solutions to multilingual
    visual question answering (mVQA), on both data and modeling fronts. We first
    propose a translation-based framework to mVQA data generation that requires
    much less human annotation efforts than the conventional approach of
    directly collection questions and answers. Then, we apply our framework to
    the multilingual captions in the Crossmodal-3600 dataset and develop an
    efficient annotation protocol to create MaXM, a test-only V

All datasets marked Unclear | JSON