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
this https URL

通过引入嵌入和舍入技术及自定义基于边缘的损失函数，将离散事件图形转换为可学习的潜在表示，并提出去噪训练过程来维持模型鲁棒性，我们提出了一种新颖的扩散事件图模型（DEGM），该模型能保证在模式生成过程中逐步优化的潜在表示中进行错误修正，实验结果表明 DEGM 优于其他最先进的基线模型。

事件骨架生成的扩散模型

A Diffusion Model for Event Skeleton Generation

We consider open domain event extraction, the task of extracting unconstraint
types of events from news clusters. A novel latent variable neural model is
constructed, which is scalable to very large corpus. A dataset is collected and
manually annotated, with task-specific evaluation metrics being designed.
Results show that the proposed unsupervised model gives better performance
compared to the state-of-the-art method for event schema induction.

提出一种扩展了事件类型的任务 - 开放域事件抽取的无监督模型，通过构建一个可扩展的隐变量神经模型，收集了大量的数据集并进行了手动注释，通过任务特定的评估指标进行了评估，与现有最先进的方法进行了比较，结果表明该模型比现有的事件模式归纳方法具有更好的性能。