Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candidate role with the target argument span. Then, a pre-trained language model scores the new passages, making the initial prediction. Our novel prompt templates can easily adapt to all events and argument types without manual effort. Next, the model regularizes the prediction by global constraints exploiting cross-task, cross-argument, and cross-event relations. Extensive experiments demonstrate our model's effectiveness: it outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, respectively, without given argument spans. We have made our code publicly available.

翻译：事件论元角色判定是事件抽取的关键子任务。以往大多数监督模型依赖昂贵的人工标注，这在开放域应用中难以实际运用。本研究提出利用全局约束与提示技术，无需任何标注和任务特定训练即可有效处理事件论元分类。具体而言，给定一个事件及其关联段落，模型首先通过前缀提示和完形填空提示生成多个新段落——其中前缀提示标明事件类型与触发词跨度，完形填空提示将每个候选角色与目标论元跨度关联。随后，预训练语言模型对这些新段落进行评分，生成初始预测。本方法设计的创新提示模板可轻松适配所有事件类型及论元类型，无需人工干预。接着，模型通过利用跨任务、跨论元及跨事件关系的全局约束对预测结果进行正则化。大量实验证明了模型的有效性：在给定论元跨度条件下，本方法在ACE和ERE数据集上的F1值分别超过最优零样本基线12.5%和10.9%；在未给定论元跨度条件下，分别提升4.3%和3.3%。相关代码已开源发布。