  Information extraction (IE) systems aim to automatically extract structured
information, such as named entities, relations between entities, and events,
from unstructured texts. While most existing work addresses a particular IE
task, universally modeling various IE tasks with one model has achieved great
success recently. Despite their success, they employ a one-stage learning
strategy, i.e., directly learning to extract the target structure given the
input text, which contradicts the human learning process. In this paper, we
propose a unified easy-to-hard learning framework consisting of three stages,
i.e., the easy stage, the hard stage, and the main stage, for IE by mimicking
the human learning process. By breaking down the learning process into multiple
stages, our framework facilitates the model to acquire general IE task
knowledge and improve its generalization ability. Extensive experiments across
four IE tasks demonstrate the effectiveness of our framework. We achieve new
state-of-the-art results on 13 out of 17 datasets. Our code is available at
\url{https://github.com/DAMO-NLP-SG/IE-E2H}.
