Enhancing Chinese Event Prediction with Prompt-Driven Knowledge Augmentation

Achieving high accuracy in Chinese event prediction remains challenging due to the prevalence of short-text events that lack sufficient background knowledge and semantic depth. Existing approaches predominantly rely on knowledge augmentation strategies that adopt a retrieval-and-integration paradigm, which often results in suboptimal performance due to the limited scope and timeliness of KGs (Knowledge Graphs). Large language models (LLMs) play a crucial role in event knowledge augmentation by serving as dynamic, context-aware knowledge generators that address the limitations of KGs. This paper introduces an enhanced method for Chinese event prediction, named CEP-PKA (Chinese Event Prediction with Prompt-driven Knowledge Augmentation). Specifically, we design a prompt engineering strategy to leverage an LLM to generate fine-grained knowledge for event elements, including triggers and arguments. We then fine-tune a pre-trained language model (PLM) to learn a joint representation of events and its associated knowledge. Experimental results in two public Chinese benchmarks demonstrate that CEP-PKA outperforms baseline models relying solely on LLMs or PLMs, achieving average accuracy improvements of 10.62% and 12.51%, respectively.