  Event extraction requires high-quality expert human annotations, which are
usually expensive. Therefore, learning a data-efficient event extraction model
that can be trained with only a few labeled examples has become a crucial
challenge. In this paper, we focus on low-resource end-to-end event extraction
and propose DEGREE, a data-efficient model that formulates event extraction as
a conditional generation problem. Given a passage and a manually designed
prompt, DEGREE learns to summarize the events mentioned in the passage into a
natural sentence that follows a predefined pattern. The final event predictions
are then extracted from the generated sentence with a deterministic algorithm.
DEGREE has three advantages to learn well with less training data. First, our
designed prompts provide semantic guidance for DEGREE to leverage DEGREE and
thus better capture the event arguments. Moreover, DEGREE is capable of using
additional weakly-supervised information, such as the description of events
encoded in the prompts. Finally, DEGREE learns triggers and arguments jointly
in an end-to-end manner, which encourages the model to better utilize the
shared knowledge and dependencies among them. Our experimental results
demonstrate the strong performance of DEGREE for low-resource event extraction.
