CALM: Commen-Sense Knowledge Augmentation for Document Image Understanding

Qinyi Du, Qingqing Wang, Keqian Li, Jidong Tian, Liqiang Xiao, Yaohui Jin

Abstract: Performance of document image understanding has been significantly fueled by encoding multi-modal information in recent years. However, existing works heavily rely on the superficial appearance of the observed data, resulting in counter-intuitive model behavior in many critical cases. To overcome this issue, this paper proposes a common-sense knowledge augmented model CALM for document image understanding tasks. It firstly produces purified representations of document contents to extract key information and learn common-sense augmented representation for inputs. Then, relevant common-sense knowledge is extracted from the external ConceptNet knowledge base, and a derived knowledge graph is built to enhance the common-sense reasoning capability of CALM jointly. In order to further highlight the importance of common-sense knowledge in document image understanding, we propose the first question-answering dataset, CS-DVQA, focused on common-sense reasoning for document images, in which questions are answered by taking both document contents and common-sense knowledge into consideration. Through extensive evaluation, the proposed CALM approach outperforms the state-of-the-art models in three document image understanding tasks, including key information extraction(from 85.37 to 86.52), document image classification(from 96.08 to 96.17), document visual question answering(from 86.72 to 88.03).