Script event prediction (SEP), aiming at predicting next event from context event sequences (i.e., scripts), has played an important role in many real-world applications such as government decision-making. While most of the existing research only depend on the top-level event prediction, they ignore the influence of other bottom levels or other relationship modeling manners. In this paper, we focus on the problem of SEP via multilevel script learning where the goal of is to explore a multistage, multiprediction and multilevel information fusion model for SEP. This is challenging in (1) simultaneously modeling of the multilevel event relationship semantic information and (2) effectively designing multilevel information fusion strategies. In this paper, we propose a new script event prediction model based on Enhanced Multilevel script learning and Dual Fusion strategies, named EMDF-Net. Specifically, EMDF-Net designs the multilevel (event/chain/segment level) script learning to model both temporal and casual information as well as the rich structural relevance via neural stacking of self-attention mechanism and graph neural networks. Then it proposes dual fusion strategies to fully integrate different-level information by nonlinear feature composition and weighted score fusion. Finally, a deep supervision strategy is utilized to end-to-end train the whole model and provide a good initialization for information fusion. Experimental results on the popular NYT corpus demonstrate the effectiveness and superiority of EMDF-Net.

中文翻译：

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接下来发生什么？结合增强的多级脚本学习和双重融合策略进行脚本事件预测

脚本事件预测（SEP）旨在从上下文事件序列（即脚本）中预测下一个事件，在政府决策等许多现实世界的应用中发挥了重要作用。而现有的研究大多只依赖于顶层事件预测，而忽略了其他底层或其他关系建模方式的影响。在本文中，我们通过多级脚本学习关注 SEP 问题，其目标是探索 SEP 的多阶段、多预测和多级信息融合模型。这在 (1) 多级事件关系语义信息的同时建模和 (2) 有效设计多级信息融合策略方面具有挑战性。在本文中，我们提出了一种新的基于E的脚本事件预测模型增强的多级脚本学习和双重融合策略，命名为 EMDF - Net 。具体来说，EMDF-Net 设计了多级（事件/链/段级）脚本学习，通过自我注意机制和图神经网络的神经堆叠来对时间和偶然信息以及丰富的结构相关性进行建模。然后提出了双重融合策略，通过非线性特征组合和加权分数融合来充分融合不同层次的信息。最后，利用深度监督策略对整个模型进行端到端训练，并为信息融合提供良好的初始化。在流行的 NYT 语料库上的实验结果证明了 EMDF-Net 的有效性和优越性。