Filling in the Gaps: Efficient Event Coreference Resolution using Graph
  Autoencoder Networks
Loic De Langhe Orphée De Clercq Veronique Hoste
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.