  Eliciting knowledge from pre-trained language models via prompt-based
learning has shown great potential in many natural language processing tasks.
Whereas, the applications for more complex tasks such as event extraction are
less studied since the design of prompt is not straightforward for the
structured event containing various triggers and arguments. % Meanwhile,
current conditional generation methods employ large encoder-decoder models,
which are costly to train and serve. In this paper, we present a novel
prompt-based approach, which elicits both the independent and joint knowledge
about different events for event argument extraction. The experimental results
on the benchmark ACE2005 dataset show the great advantages of our proposed
approach. In particular, our approach is superior to the recent advanced
methods in both fully-supervised and low-resource scenarios.
