A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction

• URL: http://arxiv.org/abs/2403.09721v1
• Date: Tue, 12 Mar 2024 08:58:07 GMT
• Title: A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction
• Authors: Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu,
• Abstract summary: Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. We propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper.
• Score: 12.286432133599355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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