metadata
license: cc-by-4.0
dataset_info:
- config_name: multieurlex-doc-en
features:
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dtype: string
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sequence:
sequence: string
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sequence:
sequence:
sequence: int64
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features:
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dtype:
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- config_name: wiki-doc-ja-img
features:
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dtype: image
- name: label
dtype:
class_label:
names:
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- config_name: wiki-doc-zh-img
features:
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dtype: image
- name: label
dtype:
class_label:
names:
'0': AcademicJournal
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configs:
- config_name: multieurlex-doc-en
data_files:
- split: train
path: multieurlex-doc-en/train-*
- split: test
path: multieurlex-doc-en/test-*
- split: validation
path: multieurlex-doc-en/validation-*
- config_name: wiki-doc-ar-img
data_files:
- split: train
path: wiki-doc-ar-img/train-*
- split: test
path: wiki-doc-ar-img/test-*
- split: validation
path: wiki-doc-ar-img/validation-*
- config_name: wiki-doc-ja-img
data_files:
- split: train
path: wiki-doc-ja-img/train-*
- split: test
path: wiki-doc-ja-img/test-*
- split: validation
path: wiki-doc-ja-img/validation-*
- config_name: wiki-doc-zh-img
data_files:
- split: train
path: wiki-doc-zh-img/train-*
- split: test
path: wiki-doc-zh-img/test-*
- split: validation
path: wiki-doc-zh-img/validation-*
Additional Information
Licensing Information
We provide MultiEURLEX with the same licensing as the original EU data (CC-BY-4.0):
© European Union, 1998-2021
The Commission’s document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
Citation Information
@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 multilingual Document AI models on cross-lingual transfer across typologically distant languages. Our datasets and findings open the door for future research into improving Document AI models.",
}