Document-level Event Argument Extraction with Entity type-aware Graph Link Prediction

Document-level event argument extraction faces challenges such as context modeling, cross-sentence correlations, and long-distance dependencies. Pre-vious researches have introduced abstract meaning representation to capture the semantic structure of documents. However, there are still issues with in-complete argument spans and misclassified argument roles. To improve the performance of the model in argument identification and classification, we propose a novel model EBGE, which involves an entity type-aware bidirec-tional heterogeneous graph in. It updates node representations by means of relational graph attention network, and then predicts arguments through node representations and span entity type embeddings. Experimental results on public datasets, WikiEvents and RAMS, demonstrate that our model achieves improvements in F1 scores on both subtasks compared to previous state-of-the-art works.