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

CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities