Document Understanding Dataset and Evaluation (DUDE)

• URL: http://arxiv.org/abs/2305.08455v3
• Date: Mon, 11 Sep 2023 10:36:41 GMT
• Title: Document Understanding Dataset and Evaluation (DUDE)
• Authors: Jordy Van Landeghem, Rub\'en Tito, {\L}ukasz Borchmann, Micha{\l} Pietruszka, Pawe{\l} J\'oziak, Rafa{\l} Powalski, Dawid Jurkiewicz, Micka\"el Coustaty, Bertrand Ackaert, Ernest Valveny, Matthew Blaschko, Sien Moens, Tomasz Stanis{\l}awek
• Abstract summary: Document Understanding dataset and evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs) We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins, and dates.
• Score: 29.78902147806488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins, and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.

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