A Multi-Modal Multilingual Benchmark for Document Image Classification

Yoshinari Fujinuma, Siddharth Varia, Nishant Sankaran, Srikar Appalaraju, Bonan Min, Yogarshi Vyas

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.
Anthology ID:
2023.findings-emnlp.958
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14361–14376
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.958
DOI:
10.18653/v1/2023.findings-emnlp.958
Bibkey:
Cite (ACL):
Yoshinari Fujinuma, Siddharth Varia, Nishant Sankaran, Srikar Appalaraju, Bonan Min, and Yogarshi Vyas. 2023. A Multi-Modal Multilingual Benchmark for Document Image Classification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14361–14376, Singapore. Association for Computational Linguistics.
Cite (Informal):
A Multi-Modal Multilingual Benchmark for Document Image Classification (Fujinuma et al., Findings 2023)
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