Document information extraction tasks performed by humans create data
consisting of a PDF or document image input, and extracted string outputs. This
end-to-end data is naturally consumed and produced when performing the task
because it is valuable in and of itself. It is naturally available, at no
additional cost. Unfortunately, state-of-the-art word classification methods
for information extraction cannot use this data, instead requiring word-level
labels which are expensive to create and consequently not available for many
real life tasks. In this paper we propose the Attend, Copy, Parse architecture,
a deep neural network model that can be trained directly on end-to-end data,
bypassing the need for word-level labels. We evaluate the proposed architecture
on a large diverse set of invoices, and outperform a state-of-the-art
production system based on word classification. We believe our proposed
architecture can be used on many real life information extraction tasks where
word classification cannot be used due to a lack of the required word-level
labels.

本文提出了一种深度神经网络模型 ——Attend，Copy，Parse，该模型可直接在端到端数据上进行训练，跳过了创建复杂的单词级标签的需求，因此被认为在许多现实生活信息提取任务中可以使用。