Event Argument Extraction
33 papers with code • 2 benchmarks • 2 datasets
Libraries
Use these libraries to find Event Argument Extraction models and implementations
ACE 2005
WikiEvents
Most implemented papers
TAGPRIME: A Unified Framework for Relational Structure Extraction
In this work, we propose to take a unified view of all these tasks and introduce TAGPRIME to address relational structure extraction problems.
GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles
We utilize this ontology to further introduce GENEVA, a diverse generalizability benchmarking dataset comprising four test suites, aimed at evaluating models' ability to handle limited data and unseen event type generalization.
EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction
zqs1943/docie
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Events are inter-related in documents.
Few-Shot Document-Level Event Argument Extraction
To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset.
Dynamic Global Memory for Document-level Argument Extraction
xinyadu/memory_docie
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ACL 2022
Extracting informative arguments of events from news articles is a challenging problem in information extraction, which requires a global contextual understanding of each document.
Code4Struct: Code Generation for Few-Shot Event Structure Prediction
As a case study, we formulate Event Argument Extraction (EAE) as converting text into event-argument structures that can be represented as a class object using code.
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction
hustminslab/bip
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Recently, prompt-tuning has attracted growing interests in event argument extraction (EAE).
Retrieval-Augmented Generative Question Answering for Event Argument Extraction
xinyadu/rgqa
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We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction.
LeTI: Learning to Generate from Textual Interactions
xingyaoww/leti
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We explore LMs' potential to learn from textual interactions (LETI) that not only check their correctness with binary labels but also pinpoint and explain errors in their outputs through textual feedback.
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model
However, existing generation-based EAE models mostly focus on problem re-formulation and prompt design, without incorporating additional information that has been shown to be effective for classification-based models, such as the abstract meaning representation (AMR) of the input passages.
