AmazonScience/MultilingualMultiModalClassification

Permissive or share-alike

The declared licence (cc-by-4.0) is classified as permissive.

Licence facts reported by the dataset and its declared sources. Not legal advice, and not a grant of permission to use the dataset.

Evidence

Declared licence: cc-by-4.0 (from the tag)
Licence files: no licence file at the top level of the repository
Access gated on Hugging Face: no

Declared sources

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Signal

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Citation text from the card

@inproceedings{fujinuma-etal-2023-multi,
    title = "A Multi-Modal Multilingual Benchmark for Document Image Classification",
    author = "Fujinuma, Yoshinari  and
      Varia, Siddharth  and
      Sankaran, Nishant  and
      Appalaraju, Srikar  and
      Min, Bonan  and
      Vyas, Yogarshi",
    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.958",
    doi = "10.18653/v1/2023.findings-emnlp.958",
    pages = "14361--14376",
    abstract = "Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents. We show that the only existing dataset for this task (Lewis et al., 2006) has several limitations and we introduce two newly curated multilingual datasets WIKI-DOC and MULTIEURLEX-DOC that overcome these limitations. We further undertake a comprehensive study of popular visually-rich document understanding or Document AI models in previously untested setting in document image classification such as 1) multi-label classification, and 2) zero-shot cross-lingual transfer setup. Experimental results show limitations of multiling

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