Low-Dimensionality Information Extraction Model for Semi-structured Documents

Most recent systems of information extraction (IE) from documents are regarded as complex models due to the large number of parameters they need and their resulting high memory footprint. In this paper, we propose a non complex model that extracts information from semi-structured documents (SSDs). We focus on the improvement of the model's input modelling that provide a low memory consumption and better performance. The SSD is modelled using graphs to benefit from its content and layout properties. A Multi-layer Graph Attention network (Multi-GAT) classifier built on the SSD graph is then used to predict the text entities. To get rid of the unknown word embeddings in this kind of document, we provide a simple and efficient method of pre-trained sub-word embeddings fusion that doesn't require any additional parameters. Our strategy for combining the multi-modal features of text, layout, and image entails concatenating the results of two Dense layers applied to the word embedding, position encoding and image embedding. Additionally, the graph adjacency matrix is built in a way to limit the graph dimension and enhance the classifier performance. All of these techniques improve the performance of our model while reducing its complexity and input dimensionality. Our model is evaluated on two artificial invoices datasets as well as one real dataset (SROIE). For the latter, we obtained a F1 score of 98.22%.