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.
