Information extraction (IE) for visually-rich documents (VRDs) has achieved SOTA performance recently thanks to the adaptation of Transformer-based language models, which shows the great potential of pre-training methods. In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new query-based IE model that employs span extraction instead of using the common sequence labeling approach. Secondly, to extend the span extraction formulation, we propose a new training task focusing on modelling the relationships among semantic entities within a document. This task enables target spans to be extracted recursively and can be used to pre-train the model or as an IE downstream task. Evaluation on three datasets of popular business documents (invoices, receipts) shows that our proposed method achieves significant improvements compared to existing models. The method also provides a mechanism for knowledge accumulation from multiple downstream IE tasks.
Abstract. A Virtual Organisation (VO) is a temporary alliance of autonomous, diverse, and geographically dispersed organisations, where the participants pool resources, information and knowledge in order to meet common objectives. This requires dynamic security policy management. We propose an authorisation policy management model called recognition of authority (ROA) which allows dynamically trusted authorities to adjust the authorisation policies for VO resources. The model supports dynamic delegation of authority, and the expansion and contraction of organizations in a VO, so that the underlying authorisation system is able to use existing user credentials issued by participating organisations to evaluate the user's access rights to VO resources.
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