Research in Document Intelligence and especially in Document Key Information Extraction (DocKIE) has been mainly solved as Token Classification problem. Recent breakthroughs in both natural language processing (NLP) and computer vision helped building document-focused pre-training methods, leveraging a multimodal understanding of the document text, layout and image modalities. However, these breakthroughs also led to the emergence of a new DocKIE subtask of extractive document Question Answering (DocQA), as part of the Machine Reading Comprehension (MRC) research field. In this work, we compare the Question Answering approach with the classical token classification approach for document key information extraction. We designed experiments to benchmark five different experimental setups : raw performances, robustness to noisy environment, capacity to extract long entities, fine-tuning speed on Few-Shot Learning and finally Zero-Shot Learning. Our research showed that when dealing with clean and relatively short entities, it is still best to use token classification-based approach, while the QA approach could be a good alternative for noisy environment or long entities use-cases.

翻译：文档智能研究，特别是文档关键信息抽取（DocKIE）领域，主要被视为令牌分类问题来解决。自然语言处理（NLP）与计算机视觉领域的最新突破，催生了面向文档的预训练方法，该方法利用文档文本、布局与图像模态的多模态理解能力。然而，这些突破也促使了机器阅读理解（MRC）研究领域中新的DocKIE子任务——抽取式文档问答（DocQA）的兴起。本研究对比了问答方法与经典令牌分类方法在文档关键信息抽取中的表现。我们设计了实验以基准测试五种不同实验配置：原始性能、噪声环境鲁棒性、长实体抽取能力、少样本学习的微调速度，以及零样本学习。研究结果表明，在处理清晰且长度较短的实体时，基于令牌分类的方法仍为最优选择；而QA方法可作为噪声环境或长实体场景的有效替代方案。