  The problem of event extraction requires detecting the event trigger and
extracting its corresponding arguments. Existing work in event argument
extraction typically relies heavily on entity recognition as a
preprocessing/concurrent step, causing the well-known problem of error
propagation. To avoid this issue, we introduce a new paradigm for event
extraction by formulating it as a question answering (QA) task that extracts
the event arguments in an end-to-end manner. Empirical results demonstrate that
our framework outperforms prior methods substantially; in addition, it is
capable of extracting event arguments for roles not seen at training time
(zero-shot learning setting).
