Modeling correlated causal-effect structure with a hypergraph for document-level event causality identification

Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Existing approaches for document-level event causality identification detect the causal relation for each pair of event mentions independently, while ignoring latent correlated cause–effect structure in a document, i.e., one cause (effect) with multiple effects (causes). We argue that identifying the causal relation of one event pair may facilitate the causality identification for other event pairs. In light of such considerations, we propose to model the correlated causal-effect structure by a hypergraph and jointly identify multiple causal relations with the same cause (effect). In particular, we propose an event-hypergraph neural encoding model, called EHNEM, for document-level event causality identification. In EHNEM, we first initialize event mentions’ embeddings via a pre-trained language model and obtain potential causal relation of each event pair via a multilayer perceptron. To capture causal correlations, we construct a hypergraph by integrating potential causal relations for the same event as a hyperedge. On the constructed event-hypergraph, we use a hypergraph convolutional network to learn the representation of each event node for final causality identification. Experiments on both EventStoryLine corpus and English-MECI corpus show that our EHNEM model significantly outperforms the state-of-the-art algorithms.