  In this paper, we propose a recent and under-researched paradigm for the task
of event detection (ED) by casting it as a question-answering (QA) problem with
the possibility of multiple answers and the support of entities. The extraction
of event triggers is, thus, transformed into the task of identifying answer
spans from a context, while also focusing on the surrounding entities. The
architecture is based on a pre-trained and fine-tuned language model, where the
input context is augmented with entities marked at different levels, their
positions, their types, and, finally, the argument roles. Experiments on the
ACE~2005 corpus demonstrate that the proposed paradigm is a viable solution for
the ED task and it significantly outperforms the state-of-the-art models.
Moreover, we prove that our methods are also able to extract unseen event
types.
