Prompt-based methods have become increasingly popular among information
extraction tasks, especially in low-data scenarios. By formatting a finetune
task into a pre-training objective, prompt-based methods resolve the data
scarce problem effectively. However, seldom do previous research investigate
the discrepancy among different prompt formulating strategies. In this work, we
compare two kinds of prompts, name-based prompt and ontology-base prompt, and
reveal how ontology-base prompt methods exceed its counterpart in zero-shot
event argument extraction (EAE) . Furthermore, we analyse the potential risk in
ontology-base prompts via a causal view and propose a debias method by causal
intervention. Experiments on two benchmarks demonstrate that modified by our
debias method, the baseline model becomes both more effective and robust, with
significant improvement in the resistance to adversarial attacks.

本文通过比较两种不同的提示策略，即基于名称的提示和基于本体的提示，揭示了本体提示方法在零样本事件论证提取中的优势，并提出了一种因果干预的去偏方法。实验表明，经过去偏处理后的基准模型不仅更为有效和鲁棒，而且在对抗攻击方面有显着的改进。