Relation Augmented Attention Transformer

Relation-augmented attention transformers enhance traditional transformer architectures by explicitly incorporating relational information between data elements, improving performance on various tasks involving structured data. Current research focuses on developing novel attention mechanisms that effectively capture both node-level and relation-level interactions within graphs, often employing bi-level attention or relation-aware attention within graph neural networks (GNNs) and transformer models. This approach leads to improved performance in diverse applications, including entity alignment, multi-person motion prediction, and document-level event extraction, by leveraging the rich contextual information provided by relational dependencies.