  We study the problem of event extraction from text data, which requires both
detecting target event types and their arguments. Typically, both the event
detection and argument detection subtasks are formulated as supervised sequence
labeling problems. We argue that the event extraction models so trained are
inherently label-hungry, and can generalize poorly across domains and text
genres.We propose a reading comprehension framework for event
extraction.Specifically, we formulate event detection as a textual entailment
prediction problem, and argument detection as a question answer-ing problem. By
constructing proper query templates, our approach can effectively distill rich
knowledge about tasks and label semantics from pretrained reading comprehension
models. Moreover, our model can be fine-tuned with a small amount of data to
boost its performance. Our experiment results show that our method performs
strongly for zero-shot and few-shot event extraction, and it achieves
state-of-the-art performance on the ACE 2005 benchmark when trained with full
supervision.
