A Diffusion Model for Event Skeleton Generation

• URL: http://arxiv.org/abs/2305.17458v1
• Date: Sat, 27 May 2023 12:19:21 GMT
• Title: A Diffusion Model for Event Skeleton Generation
• Authors: Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, Haiqin Yang
• Abstract summary: Event skeleton generation is a critical step in the temporal complex event schema induction task. Existing methods effectively address this task from a graph generation perspective. We propose a novel Diffusion Event Graph Model(DEGM) to address these issues.
• Score: 32.288113334600595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• IFH: a Diffusion Framework for Flexible Design of Graph Generative Models [53.219279193440734]
  Graph generative models can be classified into two prominent families: one-shot models, which generate a graph in one go, and sequential models, which generate a graph by successive additions of nodes and edges. This paper proposes a graph generative model, called Insert-Fill-Halt (IFH), that supports the specification of a sequentiality degree.
  arXiv  Detail & Related papers  (2024-08-23T16:24:40Z)
• Graph Generation with Diffusion Mixture [57.78958552860948]
  Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. We propose a generative framework that models the topology of graphs by explicitly learning the final graph structures of the diffusion process.
  arXiv  Detail & Related papers  (2023-02-07T17:07:46Z)
• A Graph Regularized Point Process Model For Event Propagation Sequence [2.9093633827040724]
  Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. We propose a Graph Regularized Point Process that characterizes the event interactions across nodes with neighbors. By applying a graph regularization method, GRPP provides model interpretability by uncovering influence strengths between nodes.
  arXiv  Detail & Related papers  (2022-11-21T04:49:59Z)
• NGEP: A Graph-based Event Planning Framework for Story Generation [17.049035309926637]
  We propose NGEP, a novel event planning framework which generates an event sequence by performing inference on an automatically constructed event graph. We conduct a range of experiments on multiple criteria, and the results demonstrate that our graph-based neural framework outperforms the state-of-the-art (SOTA) event planning approaches.
  arXiv  Detail & Related papers  (2022-10-19T14:49:27Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• Causal Incremental Graph Convolution for Recommender System Retraining [89.25922726558875]
  Real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN) based recommender models.
  arXiv  Detail & Related papers  (2021-08-16T04:20:09Z)
• Future is not One-dimensional: Graph Modeling based Complex Event Schema Induction for Event Prediction [90.75260063651763]
  We introduce the concept of Temporal Complex Event: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. We release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and manually constructed gold schemas.
  arXiv  Detail & Related papers  (2021-04-13T16:41:05Z)
• Neural Language Modeling for Contextualized Temporal Graph Generation [49.21890450444187]
  This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document.
  arXiv  Detail & Related papers  (2020-10-20T07:08:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.