  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.
