A Diffusion Model for Event Skeleton Generation
Fangqi Zhu Lin Zhang Jun Gao Bing Qin Ruifeng Xu Haiqin Yang
Event skeleton generation, aiming to induce an event schema skeleton graph
with abstracted event nodes and their temporal relations from a set of event
instance graphs, is a critical step in the temporal complex event schema
induction task. Existing methods effectively address this task from a graph
generation perspective but suffer from noise-sensitive and error accumulation,
e.g., the inability to correct errors while generating schema. We, therefore,
propose a novel Diffusion Event Graph Model~(DEGM) to address these issues. Our
DEGM is the first workable diffusion model for event skeleton generation, where
the embedding and rounding techniques with a custom edge-based loss are
introduced to transform a discrete event graph into learnable latent
representation. Furthermore, we propose a denoising training process to
maintain the model's robustness. Consequently, DEGM derives the final schema,
where error correction is guaranteed by iteratively refining the latent
representation during the schema generation process. Experimental results on
three IED bombing datasets demonstrate that our DEGM achieves better results
than other state-of-the-art baselines. Our code and data are available at
https://github.com/zhufq00/EventSkeletonGeneration.