A Semantic Mention Graph Augmented Model for Document-Level Event
  Argument Extraction
Jian Zhang Changlin Yang Haiping Zhu Qika Lin Fangzhi Xu Jun Liu
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.