Most RE methods work in a simplified setting, and mainly focus on training models with large amounts of human annotations to classify two given entities within one sentence into pre-defined relations. To build an effective and robust RE system that can be deployed in the real world, more complex scenarios must be further investigated.

• Collecting high-quality human annotation is expensive and time-consuming
• Many long-tail relations cannot provide large amounts of training examples
• Most facts are expressed by long context consisting of multiple sentences
• Using a predefined set to cover those relations with open-ended growth is not easy