On Leveraging Multi-Page Element Relations in Visually-Rich Documents

Thanks to the rapid progress of the digitalization process, Visually-Rich Documents (VRDs) such as PDF files or scanned documents have become among the most widespread sources of knowledge. However, Question Answering on VRDs is challenged by the presence of multi-page relationships between document elements such as tables, figures, sections. This paper addresses a specific Visual Question Answering subtask from VDRs where answer generation leverages pairwise element relations in multi-page documents. We explore the performance of text-only and multimodal Transformer-based architectures as well as open-source Large Language Models. The results show that multimodal Transformers outperform the other tested methods, particularly when training samples contain explicit textual references to the elements in the document layout.