Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance

Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart. However, most prior works focus on capturing the relations between candidate arguments and the event trigger in each event, ignoring two crucial points: a) non-argument contextual clue information; b) the relevance among argument roles. In this paper, we propose a SCPRG (Span-trigger-based Contextual Pooling and latent Role Guidance) model, which contains two novel and effective modules for the above problem. The Span-Trigger-based Contextual Pooling(STCP) adaptively selects and aggregates the information of non-argument clue words based on the context attention weights of specific argument-trigger pairs from pre-trained model. The Role-based Latent Information Guidance (RLIG) module constructs latent role representations, makes them interact through role-interactive encoding to capture semantic relevance, and merges them into candidate arguments. Both STCP and RLIG introduce no more than 1% new parameters compared with the base model and can be easily applied to other event extraction models, which are compact and transplantable. Experiments on two public datasets show that our SCPRG outperforms previous state-of-the-art methods, with 1.13 F1 and 2.64 F1 improvements on RAMS and WikiEvents respectively. Further analyses illustrate the interpretability of our model.

A Dual-Perception Graph Neural Network with Multi-hop Graph Generator

Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs excessively rely on topological structures and aggregate multi-hop neighborhood information by simply stacking network layers, which may introduce superfluous noise information, limit the expressive power of GNNs and lead to the over-smoothing problem ultimately. In light of this, we propose a novel Dual-Perception Graph Neural Network (DPGNN) to address these issues. In DPGNN, we utilize node features to construct a feature graph, and perform node representations learning based on the original topology graph and the constructed feature graph simultaneously, which conduce to capture the structural neighborhood information and the feature-related information. Furthermore, we design a Multi-Hop Graph Generator (MHGG), which applies a node-to-hop attention mechanism to aggregate node-specific multi-hop neighborhood information adaptively. Finally, we apply self-ensembling to form a consistent prediction for unlabeled node representations. Experimental results on five datasets with different topological structures demonstrate that our proposed DPGNN outperforms all the latest state-of-the-art models on all datasets, which proves the superiority and versatility of our model. The source code of our model is available at https://github.com.