  Event detection refers to identifying event occurrences in a text and
comprises of two subtasks; event identification and classification. We present
EDM3, a novel approach for Event Detection that formulates three generative
tasks: identification, classification, and combined detection. We show that
EDM3 helps to learn transferable knowledge that can be leveraged to perform
Event Detection and its subtasks concurrently, mitigating the error propagation
inherent in pipelined approaches. Unlike previous dataset- or domain-specific
approaches, EDM3 utilizes the existing knowledge of language models, allowing
it to be trained over any classification schema. We evaluate EDM3 on multiple
event detection datasets: RAMS, WikiEvents, MAVEN, and MLEE, showing that EDM3
outperforms 1) single-task performance by 8.4% on average and 2) multi-task
performance without instructional prompts by 2.4% on average. We obtain SOTA
results on RAMS (71.3% vs. 65.1% F-1) and competitive performance on other
datasets. We analyze our approach to demonstrate its efficacy in low-resource
and multi-sentence settings. We also show the effectiveness of this approach on
non-standard event configurations such as multi-word and multi-class event
triggers. Overall, our results show that EDM3 is a promising approach for Event
Detection that has the potential for real-world applications.
