Bidirectional Prompt-Tuning for Event Argument Extraction with Topic and Entity Embeddings

In recent years,prompt learning has been widely applied in the field of natural language processing.According to research,argument roles are highly semantically related to topics in text,and existing prompt tuning methods overlook entity information and interactions between arguments.Therefore,this paper proposes a bidirectional prompt tuning event argument extraction model(TEPEAE) that integrates topic and entity embeddings.Firstly,topic features are extracted using a topic model and embedded into a topic representation.Secondly,prompt templates are constructed based on trigger words,arguments,and entity information,incorporating topic embeddings into the template.Thirdly,masked language model(MLM) is utilized to predict the role label for each entity.Finally,labels are mapped from the label word space to the argument role space.Experiments on ACE2005-EN and ERE-EN datasets show that TEPEAE outperforms baseline models and achieves 79.53% and 78.60% in terms of F1,respectively,which demonstrates the effectiveness of TEPEAE.Moreover,it continues to demonstrate exceptional performance in low-resource scenarios,further proving its enhanced robustness.