CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities
This work addresses event coreference resolution for natural language processing, offering an incremental improvement by integrating human-summarized rules into a prompt-based framework.
The authors tackled event coreference resolution by proposing CorefPrompt, a prompt-based method that transforms the task into a cloze-style MLM task and incorporates auxiliary compatibility prompts, achieving state-of-the-art performance on a benchmark.
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.