多重图的提示学习和注意的融合,以完成事件图的完成.
Chao Liang1, Bang Wang2, Chuanhong Zhan1
1School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
概括
本研究介绍了事件图表完成 (EGC) 任务,以预测缺失的事件关系. 拟议的多重图速学习和注意融合 (PLAF) 模型有效地提高了事件图的完整性和实用性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 图形神经网络的神经网络
背景情况:
- 异质事件图 (EGs) 经常缺少关系信息,限制了它们的下游应用实用性.
- 现有的方法很难有效地建模和预测EG内部的多种相互联系的关系.
研究的目的:
- 引入并解决新的事件图表完成 (EGC) 任务,用于预测异质EG中缺少的多关系.
- 提出一个新的模型,多重图即时学习和注意力融合 (PLAF),以提高EG的完整性.
主要方法:
- 开发了PLAF模型,结合了双图即时学习 (DGPL) 和多重图注意网络 (MGAT).
- 通过事件三重序列,DGPL编码了EG结构和语义.
- MGAT学习事件表示,使用跨图和跨图的注意力跨同质子图.
- 一个聚合关系预测模块 (ARPM) 结合了预测,以实现可靠的完成.
主要成果:
- 与最先进的方法相比,PLAF模型在预测缺失关系方面表现优越.
- 对新建的EGC-MAVEN数据集进行了广泛的实验,验证了该模型的有效性.
- 结果证实,模拟多关系交互性地提高了预测准确性.
结论:
- 拟议的PLAF模型有效地解决了事件图完成任务.
- 这种方法提高了异质事件图的完整性和实用性,用于各种应用.
- 这项工作为事件图中多关系预测建立了新的基准.
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