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

Information Extraction from Documents: Question Answering vs Token Classification in real-world setups