  The traditional way of sentence-level event detection involves two important
subtasks: trigger identification and trigger classifications, where the
identified event trigger words are used to classify event types from sentences.
However, trigger classification highly depends on abundant annotated trigger
words and the accuracy of trigger identification. In a real scenario,
annotating trigger words is time-consuming and laborious. For this reason, we
propose a trigger-free event detection model, which transforms event detection
into a two-tower model based on machine reading comprehension and prompt
learning. Compared to existing trigger-based and trigger-free methods,
experimental studies on two event detection benchmark datasets (ACE2005 and
MAVEN) have shown that the proposed approach can achieve competitive
performance.
