Xiang Wei, Liu Cheng, Wang Bang
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China.
Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China.
Neural Netw. 2025 Apr;184:107080. doi: 10.1016/j.neunet.2024.107080. Epub 2024 Dec 28.
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neural network for event node representation learning. However, not all such connections contribute to augment node representation for causality identification. We argue that the events' causal relations in a document are often interdependent, i.e., multiple causes with one effect, and identifying one cause for an effect may facilitate the identification of another cause of the same effect. In this paper, we use a hypergraph to model such events' causal relations as the document causal structure, and propose a neural causal hypergraph model (NCHM) for event causality identification. In NCHM, we design a pairwise event semantics learning module (PES) based on prompt learning to learn the pairwise event representation as well as the pairwise causal connections between two events. A document causal hypergraph is then constructed based on pairwise causal connections. We also design a document causal structure learning module (DCS) with a hypergraph convolutional neural network to learn document-wise events' representations. Finally, two kinds of representations are concatenated for document-level event causality identification. Experiments on both EventStoryLine and English-MECI corpus show that our NCHM significantly outperforms the state-of-the-art algorithms.
文档级事件因果关系识别（ECI）旨在检测文档中事件提及之间的因果关系。最近的一些方法使用图神经网络进行事件节点表示学习，对事件之间的各种连接（如句法依存关系等）进行建模。然而，并非所有这些连接都有助于增强用于因果关系识别的节点表示。我们认为，文档中事件的因果关系通常是相互依存的，即多个原因导致一个结果，识别一个结果的一个原因可能有助于识别同一结果的另一个原因。在本文中，我们使用超图将此类事件的因果关系建模为文档因果结构，并提出一种用于事件因果关系识别的神经因果超图模型（NCHM）。在NCHM中，我们基于提示学习设计了一个成对事件语义学习模块（PES），以学习成对事件表示以及两个事件之间的成对因果连接。然后根据成对因果连接构建文档因果超图。我们还使用超图卷积神经网络设计了一个文档因果结构学习模块（DCS），以学习文档级事件的表示。最后，将两种表示连接起来用于文档级事件因果关系识别。在EventStoryLine和English-MECI语料库上的实验表明，我们的NCHM显著优于现有算法。
