  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.
