论文标题
基于因果干预的迅速辩护事件参数提取
Causal Intervention-based Prompt Debiasing for Event Argument Extraction
论文作者
论文摘要
在信息提取任务中，尤其是在低数据方案中，基于及时的方法已经变得越来越流行。通过将Finetune任务格式化为预训练目标，基于及时的方法，可以有效地解决数据稀缺问题。但是，以前的研究很少研究不同的及时制定策略之间的差异。在这项工作中，我们比较了两种提示，基于名称的提示和本体论提示，并揭示本体论提示方法如何超过其在零照片事件参数提取（EAE）中。此外，我们通过因果观点分析了本体论提示中的潜在风险，并通过因果干预提出了Debias方法。在两个基准上进行的实验表明，通过我们的Debias方法修改，基线模型变得更加有效，更健壮，并且对对抗性攻击的抵抗力显着提高。
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.
