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.

翻译：事件共指消解（ECR）旨在将指向同一真实世界事件的事件提及聚类为一组。以往研究大多采用"先编码，后评分"的框架，使得共指判断依赖于事件编码。此外，当前方法难以利用人工总结的ECR规则（例如共指事件应具有相同的事件类型）来指导模型。为解决这两个问题，我们提出一种基于提示的方法CorefPrompt，将ECR转化为完形填空式的MLM（掩码语言模型）任务。这允许在单一模板内同时进行事件建模和共指判别，且上下文完全共享。此外，我们引入两个辅助提示任务——事件类型兼容性和论元兼容性——来显式展示ECR的推理过程，帮助模型做出最终预测。实验结果表明，我们的方法CorefPrompt在最新（SOTA）基准测试中表现优异。