@inproceedings{zhu-etal-2023-diffusion,
title = "A Diffusion Model for Event Skeleton Generation",
author = "Zhu, Fangqi and
Zhang, Lin and
Gao, Jun and
Qin, Bing and
Xu, Ruifeng and
Yang, Haiqin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.800",
doi = "10.18653/v1/2023.findings-acl.800",
pages = "12630--12641",
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 representations. 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 representations 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 \url{https://github.com/zhufq00/EventSkeletonGeneration}.",
}
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<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 representations. 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 representations 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.</abstract>
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%0 Conference Proceedings
%T A Diffusion Model for Event Skeleton Generation
%A Zhu, Fangqi
%A Zhang, Lin
%A Gao, Jun
%A Qin, Bing
%A Xu, Ruifeng
%A Yang, Haiqin
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhu-etal-2023-diffusion
%X 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 representations. 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 representations 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.
%R 10.18653/v1/2023.findings-acl.800
%U https://aclanthology.org/2023.findings-acl.800
%U https://doi.org/10.18653/v1/2023.findings-acl.800
%P 12630-12641
Markdown (Informal)
[A Diffusion Model for Event Skeleton Generation](https://aclanthology.org/2023.findings-acl.800) (Zhu et al., Findings 2023)
ACL
- Fangqi Zhu, Lin Zhang, Jun Gao, Bing Qin, Ruifeng Xu, and Haiqin Yang. 2023. A Diffusion Model for Event Skeleton Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 12630–12641, Toronto, Canada. Association for Computational Linguistics.