Nakano "Best Paper Award" at DAS 2022 for Thibault Douzon

On May 25th 2022, Thibault Douzon, a PhD student in the Imagine team in partnership with Esker, was awarded the Nakano "Best Paper" award for his article entitled "Improving Information Extraction in Business Documents with Specific Pre-Training". His work, carried out under the direction of Stefan Duffner, Christophe Garcia, and Jérémy Espinas, was presented at the 15th edition of the international workshop on Document Analysis Systems (DAS) at La Rochelle.


Transformer-based Language Models are widely used in Natural Language Processing related tasks. Thanks to their pre-training, they have been successfully adapted to Information Extraction in business documents. However, most pre-training tasks proposed in the literature for business documents are too generic and not sufficient to learn more complex structures.
In this paper, we use
LayoutLM, a language model pre-trained on a collection of business documents,
and introduce two new pre-training tasks that further improve its capacity to extract relevant information. 
The first is aimed at better understanding the complex layout of documents, and the second focuses on  numeric values and their order of magnitude. 
These tasks force the model to learn better-contextualized representations of the scanned documents.
We further introduce a new post-processing algorithm to decode *BIESO* tags in Information Extraction that performs better with complex entities.
Our method significantly improves extraction performance on both public (from 93.88 to 95.50 F1 score) and private (from 84.35 to 84.84 F1 score) datasets composed of expense receipts, invoices, and purchase orders.

Conference link: