  Event Extraction bridges the gap between text and event signals. Based on the
assumption of trigger-argument dependency, existing approaches have achieved
state-of-the-art performance with expert-designed templates or complicated
decoding constraints. In this paper, for the first time we introduce the
prompt-based learning strategy to the domain of Event Extraction, which
empowers the automatic exploitation of label semantics on both input and output
sides. To validate the effectiveness of the proposed generative method, we
conduct extensive experiments with 11 diverse baselines. Empirical results show
that, in terms of F1 score on Argument Extraction, our simple architecture is
stronger than any other generative counterpart and even competitive with
algorithms that require template engineering. Regarding the measure of recall,
it sets new overall records for both Argument and Trigger Extractions. We
hereby recommend this framework to the community, with the code publicly
available at https://git.io/GDAP.
