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.

本文探讨文档人工智能 (Document AI) 社区重新评估当前的方法学，并挑战创建更具实际意义的基准标准的任务。文档理解数据集和评估 (DUDE) 旨在纠正在理解视觉丰富文档方面的研究进展。我们提出了新的数据集，其中包含来自各个行业、领域和多页的视觉丰富文档的各种问题、答案和布局。此外，我们通过创建多任务和多领域的评估设置来推动当前方法的界限，以更准确地模拟真实世界中强大的泛化和适应低资源环境的需求。通过 DUDE 旨在为社区树立一个更实用、长期的基准标准，并希望它将导致未来的扩展和贡献，以应对实际挑战。最后，我们的工作说明了在文档人工智能中寻找更有效的方式来对语言、图像和布局进行建模的重要性。