We introduce a novel and efficient method for Event Coreference Resolution
(ECR) applied to a lower-resourced language domain. By framing ECR as a graph
reconstruction task, we are able to combine deep semantic embeddings with
structural coreference chain knowledge to create a parameter-efficient family
of Graph Autoencoder models (GAE). Our method significantly outperforms
classical mention-pair methods on a large Dutch event coreference corpus in
terms of overall score, efficiency and training speed. Additionally, we show
that our models are consistently able to classify more difficult coreference
links and are far more robust in low-data settings when compared to
transformer-based mention-pair coreference algorithms.

我们介绍了一种新颖高效的事件共指消解方法，将事件共指消解作为图重构任务，通过将深度语义嵌入与结构共指链知识相结合，创建了一系列参数高效的图自编码器模型（GAE）。我们的方法在荷兰的一个大型事件共指语料库上，在综合得分、效率和训练速度方面明显优于经典的提及对方法。此外，我们展示了我们的模型在更难的共指链接分类和低数据环境中相对于基于变换器的提及对共指算法更具鲁棒性。