  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.
