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.

翻译：事件提取需要高质量的专家人员说明,通常费用很高。 因此,学习一个数据效率高的事件提取模型,该模型只能用几个标签的例子进行培训,这已成为一项关键的挑战。 在本文件中,我们侧重于低资源端到端事件提取,并提出DEGREE,这是一个数据效率模型,将事件提取作为一种有条件的生成问题。鉴于一个通道和手工设计的快速,DEGREE学会了对进入自然句部分所述事件进行总结,并遵循预先确定的模式。随后,从生成的句子中提取最后的事件预测,用一种确定式算法进行。DEGREE有三个优势是学习,用较少的培训数据来学习。首先,我们设计的提示为DEGREE提供语义性指导,以利用DEGREE,从而更好地捕捉事件论证。此外,DEGREE能够使用更多薄弱的监管性信息,例如快速编码的事件描述。最后,DEGREE学会以最终的方式联合进行触发和论证,从而鼓励模型更好地利用共享的知识和依赖性资源提取活动。