Self-adaptive Prompt-tuning for Event Extraction in Ancient Chinese Literature

Extracting different types of war events from ancient Chinese literature is significant, as war is an important factor in driving the development of Chinese history. The existing trend of event extraction models utilizes template-based generative approaches, which do not take into account the brevity and obscurity of ancient Chinese, as well as the diversity of templates for similar event types. In this paper, we propose a novel Knowledge Graph-based generative event extraction framework with a self-Adaptive Prompt (KGAP) for ancient Chinese war. Specifically, we construct a self-adaptive prompt, which considers its unique trigger words for different types of wars and is designed to solve the problem of the similarity in events. Moreover, we construct a semantic knowledge graph of ancient literature, assisting the pre-trained language model to better understand the ancient Chinese text. Since there is no public dataset for the ancient Chinese event extraction task, we provide an event extraction dataset and conduct experiments on it. Experimental results show that our model is more state-of-the-art than both the classification-based and generative-based methods for event extraction in ancient Chinese literature.