Document-Level Multi-Event Extraction with Event Proxy Nodes and
  Hausdorff Distance Minimization
Xinyu Wang Lin Gui Yulan He
Document-level multi-event extraction aims to extract the structural
information from a given document automatically. Most recent approaches usually
involve two steps: (1) modeling entity interactions; (2) decoding entity
interactions into events. However, such approaches ignore a global view of
inter-dependency of multiple events. Moreover, an event is decoded by
iteratively merging its related entities as arguments, which might suffer from
error propagation and is computationally inefficient. In this paper, we propose
an alternative approach for document-level multi-event extraction with event
proxy nodes and Hausdorff distance minimization. The event proxy nodes,
representing pseudo-events, are able to build connections with other event
proxy nodes, essentially capturing global information. The Hausdorff distance
makes it possible to compare the similarity between the set of predicted events
and the set of ground-truth events. By directly minimizing Hausdorff distance,
the model is trained towards the global optimum directly, which improves
performance and reduces training time. Experimental results show that our model
outperforms previous state-of-the-art method in F1-score on two datasets with
only a fraction of training time.