  We present VESPA, an intentionally simple yet novel zero-shot system for
layout, locale, and domain agnostic document extraction. In spite of the
availability of large corpora of documents, the lack of labeled and validated
datasets makes it a challenge to discriminatively train document extraction
models for enterprises. We show that this problem can be addressed by simply
transferring the information extraction (IE) task to a natural language
Question-Answering (QA) task without engineering task-specific architectures.
We demonstrate the effectiveness of our system by evaluating on a closed corpus
of real-world retail and tax invoices with multiple complex layouts, domains,
and geographies. The empirical evaluation shows that our system outperforms 4
prominent commercial invoice solutions that use discriminatively trained models
with architectures specifically crafted for invoice extraction. We extracted 6
fields with zero upfront human annotation or training with an Avg. F1 of 87.50.
