A Diffusion Model over Directed Acyclic Graphs for Event Schema Generation

Event schema generation is crucial for understanding the structure and temporal relationships of complex events. In this paper, we introduce a novel Directed Acyclic Graph Diffusion Model (DAGDM) that integrates DAG characteristics within a diffusion framework to enhance the effectiveness of schema generation. Our method leverages DAG positional embeddings to capture the hierarchical structure of nodes within graphs, while employing a reachability-based attention to better extract structural relationships between events. To this end, we design a cross-generation strategy that separately generates event sequence and adjacency matrix. Experiments show that our model effectively captures long-range event sequences, significantly enhancing schema generation for complex events. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>