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.

翻译：我们提出了一种新颖且高效的方法，用于面向低资源语言领域的事件共指消解（ECR）。通过将ECR重构为图学习任务，我们能够将深度语义嵌入与结构化共指链知识相结合，构建参数高效的图自编码器（GAE）模型族。在荷兰语事件共指大规模语料库上，我们的方法在整体得分、效率和训练速度方面显著优于传统提及对方法。此外，实验表明，与基于Transformer的提及对共指算法相比，我们的模型能持续更准确地分类复杂共指链接，并在低数据场景下展现出更强的鲁棒性。