DUE: End-to-End Document Understanding Benchmark

Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021)

Bibtex Paper Reviews And Public Comment » Supplemental

Authors

Łukasz Borchmann, Michał Pietruszka, Tomasz Stanislawek, Dawid Jurkiewicz, Michał Turski, Karolina Szyndler, Filip Graliński

Abstract

Understanding documents with rich layouts plays a vital role in digitization and hyper-automation but remains a challenging topic in the NLP research community. Additionally, the lack of a commonly accepted benchmark made it difficult to quantify progress in the domain. To empower research in this field, we introduce the Document Understanding Evaluation (DUE) benchmark consisting of both available and reformulated datasets to measure the end-to-end capabilities of systems in real-world scenarios.The benchmark includes Visual Question Answering, Key Information Extraction, and Machine Reading Comprehension tasks over various document domains and layouts featuring tables, graphs, lists, and infographics. In addition, the current study reports systematic baselines and analyzes challenges in currently available datasets using recent advances in layout-aware language modeling. We open both the benchmarks and reference implementations and make them available at https://duebenchmark.com and https://github.com/due-benchmark.