  The task of event detection and classification is central to most information
retrieval applications. We show that a Transformer based architecture can
effectively model event extraction as a sequence labeling task. We propose a
combination of sentence level and token level training objectives that
significantly boosts the performance of a BERT based event extraction model.
Our approach achieves a new state-of-the-art performance on ACE 2005 data for
English and Chinese. We also test our model on ERE Spanish, achieving an
average gain of 2 absolute F1 points over prior best performing model.
