Information extraction: from graph neural networks to transformers - Dr. Augusto Stoffel (dida)

*dida conference 2023* *Speaker:* Dr. Augusto Stoffel (dida) *Full Title:* Information extraction: from graph neural networks to transformers *Abstract:* The last years have seen much research activity in the field of information extraction from semi-structured documents containing tables and related visual structures. Until recently, graph neural networks were among the state-of-the-art solutions for such tasks. By now, specialized transformer-based architectures have been developed. The latter have much stronger text processing capabilities and pretrained weights on large amounts of data are readily available. On the other hand, GNN models require drastically less compute resources, and one could argue that there isn't much language modeling involved in a typical information extraction task. This brings up the question: How do these two classes of models compare in practice? This talk will provide a survey of the models and attempt to answer this question based on our project experience and internal research. Hosted by dida (https://dida.do).