Recent approaches in literature have exploited the multi-modal information in
documents (text, layout, image) to serve specific downstream document tasks.
However, they are limited by their - (i) inability to learn cross-modal
representations across text, layout and image dimensions for documents and (ii)
inability to process multi-page documents. Pre-training techniques have been
shown in Natural Language Processing (NLP) domain to learn generic textual
representations from large unlabelled datasets, applicable to various
downstream NLP tasks. In this paper, we propose a multi-task learning-based
framework that utilizes a combination of self-supervised and supervised
pre-training tasks to learn a generic document representation applicable to
various downstream document tasks. Specifically, we introduce Document Topic
Modelling and Document Shuffle Prediction as novel pre-training tasks to learn
rich image representations along with the text and layout representations for
documents. We utilize the Longformer network architecture as the backbone to
encode the multi-modal information from multi-page documents in an end-to-end
fashion. We showcase the applicability of our pre-training framework on a
variety of different real-world document tasks such as document classification,
document information extraction, and document retrieval. We evaluate our
framework on different standard document datasets and conduct exhaustive
experiments to compare performance against various ablations of our framework
and state-of-the-art baselines.

本文提出了基于多任务学习和预训练技术的框架，利用文本、版面和图像的多模态信息学习通用文档表示，以支持多种下游文档任务，并在标准文档数据集上进行了广泛实验。