Information extraction: from graph neural networks to transformers

This talk aims to compare two prominent classes of models used in information extraction from semi-structured documents: Graph Neural Networks (GNNs) and specialized transformer-based architectures. While transformers are renowned for their text processing capabilities and come with pretrained weights, GNNs have the benefit of requiring much less computational power. The objective is to evaluate how these two types of models perform in practical scenarios, based on both project experience and internal research.