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Found 6 papers, 1 papers with code
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events.
MLPs Compass: What is learned when MLPs are combined with PLMs?
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field.
Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction.
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.
Ranked #1 on
Event Argument Extraction
on WikiEvents
(F1 metric)
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction.
