  Event extraction for the clinical domain is an under-explored research area.
The lack of training data along with the high volume of domain-specific
terminologies with vague entity boundaries makes the task especially
challenging. In this paper, we introduce DICE, a robust and data-efficient
generative model for clinical event extraction. DICE frames event extraction as
a conditional generation problem and introduces a contrastive learning
objective to accurately decide the boundaries of biomedical mentions. DICE also
trains an auxiliary mention identification task jointly with event extraction
tasks to better identify entity mention boundaries, and further introduces
special markers to incorporate identified entity mentions as trigger and
argument candidates for their respective tasks. To benchmark clinical event
extraction, we compose MACCROBAT-EE, the first clinical event extraction
dataset with argument annotation, based on an existing clinical information
extraction dataset MACCROBAT. Our experiments demonstrate state-of-the-art
performances of DICE for clinical and news domain event extraction, especially
under low data settings.
