Prompt for extraction: Multiple templates choice model for event extraction

Event Extraction (EE) is an essential task in natural language processing that aims to mine events occurring in event mentions represent events using event records, which usually consist of event types, trigger words, argument elements corresponding to roles in the event types. Recently, prompt-based generative models have been developed to extract events. However, these prompt-based generative studies have ignored the fact that the strong language comprehension capability of the pre-trained language model (PLM) can analyze extract the potential role relationships in multiple templates for more information that can help extract argument elements. To determine the extended templates that can help the model for event extraction, we propose the multiple template choice model (MTCM), which designs an extended event type mining module to automatically mine the extended event types in the event mention uses the templates corresponding to the extended event types to interact with the template, corresponding to the currently to-be-extracted event type of event mention, in a multi-template information interaction, which gives the model more information guides the PLM for event extraction. To validate our model, we used two widely used datasets in the event extraction domain, ACE2005-EN ERE-EN. The experimental results show that our model achieves state-of-the-art performance on the ACE2005-EN dataset significantly improves the ERE dataset. In addition, according to the results, our model can be effectively adapted to low-resource environments.