Zero-shot Task Transfer for Invoice Extraction via Class-aware QA Ensemble

TL;DR

VESPA is a simple zero-shot document extraction system that transfers invoice extraction to a QA task, outperforming specialized commercial solutions without requiring labeled training data.

Contribution

The paper introduces VESPA, a novel zero-shot approach that leverages QA for invoice extraction, eliminating the need for task-specific architectures or labeled datasets.

Findings

Outperforms 4 commercial invoice solutions

Achieves an average F1 score of 87.50 for 6 fields

Works across diverse layouts, domains, and geographies

Abstract

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.