Event coreference resolution (ECR) aims to group event mentions referring to
the same real-world event into clusters. Most previous studies adopt the
"encoding first, then scoring" framework, making the coreference judgment rely
on event encoding. Furthermore, current methods struggle to leverage
human-summarized ECR rules, e.g., coreferential events should have the same
event type, to guide the model. To address these two issues, we propose a
prompt-based approach, CorefPrompt, to transform ECR into a cloze-style MLM
(masked language model) task. This allows for simultaneous event modeling and
coreference discrimination within a single template, with a fully shared
context. In addition, we introduce two auxiliary prompt tasks, event-type
compatibility and argument compatibility, to explicitly demonstrate the
reasoning process of ECR, which helps the model make final predictions.
Experimental results show that our method CorefPrompt performs well in a
state-of-the-art (SOTA) benchmark.

事件指代消解的提问式模板方法（CorefPrompt）在单一模板中实现了事件建模和指代消解，并通过两个辅助任务明确展示了推理过程，提高了模型的预测能力。