Document-level Event Argument Extraction (DEAE) aims to identify arguments
and their specific roles from an unstructured document. The advanced approaches
on DEAE utilize prompt-based methods to guide pre-trained language models
(PLMs) in extracting arguments from input documents. They mainly concentrate on
establishing relations between triggers and entity mentions within documents,
leaving two unresolved problems: a) independent modeling of entity mentions; b)
document-prompt isolation. To this end, we propose a semantic mention Graph
Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM
constructs a semantic mention graph that captures relations within and between
documents and prompts, encompassing co-existence, co-reference and co-type
relations. Furthermore, we introduce an ensembled graph transformer module to
address mentions and their three semantic relations effectively. Later, the
graph-augmented encoder-decoder module incorporates the relation-specific graph
into the input embedding of PLMs and optimizes the encoder section with
topology information, enhancing the relations comprehensively. Extensive
experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness
of our approach, surpassing baseline methods and achieving a new
state-of-the-art performance.

本研究提出了一个语义提及图增强模型，通过构建语义提及图来解决文档级事件论证抽取中的两个问题，并将关系特定的图融入到预训练语言模型的输入嵌入中，通过拓扑信息优化编码部分，从而全面增强关系，并在实验中取得了超越基线方法和达到新的最先进性能的结果。