Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zero-shot Task Transfer for Invoice Extraction via Class-aware QA
  Ensemble   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.

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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: hep-lat