  Event extraction is challenging due to the complex structure of event records
and the semantic gap between text and event. Traditional methods usually
extract event records by decomposing the complex structure prediction task into
multiple subtasks. In this paper, we propose Text2Event, a
sequence-to-structure generation paradigm that can directly extract events from
the text in an end-to-end manner. Specifically, we design a
sequence-to-structure network for unified event extraction, a constrained
decoding algorithm for event knowledge injection during inference, and a
curriculum learning algorithm for efficient model learning. Experimental
results show that, by uniformly modeling all tasks in a single model and
universally predicting different labels, our method can achieve competitive
performance using only record-level annotations in both supervised learning and
transfer learning settings.
