Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
arxiv(2023)
摘要
Document-level event argument extraction is a crucial yet challenging task
within the field of information extraction. Current mainstream approaches
primarily focus on the information interaction between event triggers and their
arguments, facing two limitations: insufficient context interaction and the
ignorance of event correlations. Here, we introduce a novel framework named
CARLG (Contextual Aggregation of clues and Role-based Latent Guidance),
comprising two innovative components: the Contextual Clues Aggregation (CCA)
and the Role-based Latent Information Guidance (RLIG). The CCA module leverages
the attention weights derived from a pre-trained encoder to adaptively
assimilates broader contextual information, while the RLIG module aims to
capture the semantic correlations among event roles. We then instantiate the
CARLG framework into two variants based on two types of current mainstream EAE
approaches. Notably, our CARLG framework introduces less than 1
yet significantly improving the performance. Comprehensive experiments across
the RAMS, WikiEvents, and MLEE datasets confirm the superiority of CARLG,
showing significant superiority in terms of both performance and inference
speed compared to major benchmarks. Further analyses demonstrate the
effectiveness of the proposed modules.
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