Papers with Event argument extraction
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)
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| Challenge: | Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data. |
| Approach: | They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies. |
| Outcome: | The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives. |
Explicit Role Interaction Network for Event Argument Extraction (2022.findings-emnlp)
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| Challenge: | Existing methods extract arguments of each role independently, ignoring the relationship between different roles. |
| Approach: | They propose a neural model that captures the correlations between different argument roles within an event. |
| Outcome: | Extensive experiments on the benchmark dataset ACE2005 show the superiority of the proposed model over existing methods. |
Contextualized Soft Prompts for Extraction of Event Arguments (2023.findings-acl)
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| Challenge: | Existing prompt-based methods for event argument extraction rely on discrete and manually-designed prompts that cannot exploit specific context for each example. |
| Approach: | They propose a prompt-based method that introduces soft prompts to facilitate encoding of individual example context and multiple relevant documents to boost EAE. |
| Outcome: | The proposed method extensively evaluates on benchmark datasets to demonstrate its benefits with state-of-the-art performance. |
Retrieval-Augmented Generative Question Answering for Event Argument Extraction (2022.emnlp-main)
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| Challenge: | Existing methods to extract arguments from documents are based on generating and post-processing a complex target sequence (template). |
| Approach: | They propose a retrieval-augmented generative QA model that retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers. |
| Outcome: | The proposed model outperforms prior methods across fully supervised, domain transfer, and fewshot learning settings and compares with clustering-based sampling strategies. |
Resource-Enhanced Neural Model for Event Argument Extraction (2020.findings-emnlp)
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| Challenge: | Existing work on event argument extraction (EE) is limited due to data scarcity and lack of a model encoder. |
| Approach: | They propose to capture the long-range dependency between an event trigger and a distant event argument using unlabeled data. |
| Outcome: | Experiments on the English ACE 2005 benchmark show that the proposed method achieves a new state-of-the-art. |
REAR: Reinforced Reasoning Optimization for Event Argument Extraction with Relation-Aware Support (2025.findings-emnlp)
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| Challenge: | Existing methods for EAE restrict integration of relation-level semantics, thereby overlooking the complementary cues from RE. |
| Approach: | They propose a Relation-aware EAE Reinforced optimization framework that integrates relation-level cues from RE into the Large Language Model (LLM) |
| Outcome: | The proposed framework surpasses existing decoder-only methods on the ACE-E, ACE+ and ERE benchmarks. |
Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)
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| Challenge: | Existing models do not build dependency information among event argument roles . Existing methods do not learn the interactions between different roles based on event structure . |
| Approach: | They propose an intra-event and inter-e event dependency-aware graph network to model dependencies between roles . they use event structure as the fundamental unit to construct role dependencies within events . |
| Outcome: | The proposed model improves on the ACE05, RAMS, and WikiEvents datasets. |
Few-Shot Document-Level Event Argument Extraction (2023.acl-long)
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| Challenge: | Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. |
| Approach: | They propose a Few-Shot Document-Level Event Argument Extraction benchmark to capture event arguments that actually spread across sentences in documents. |
| Outcome: | The proposed task is very challenging with low performance and limited learning process . argument extraction depends on context from multiple sentences and learning process limited to very few examples . |
ULTRA: Unleash LLMs’ Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement (2024.findings-acl)
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| Challenge: | Structural extraction of events within discourse is critical for event-centric understanding . document-level EAE focuses on arguments that are scattered across an entire document . ULTRA is a hierarchical framework that extracts event arguments more cost-effectively . |
| Approach: | They propose a hierarchical framework that extracts event arguments more cost-effectively . ULTRA sequentially reads text chunks of a document to generate a candidate argument set . they propose to use a supervised model to find the exact boundary of an argument . |
| Outcome: | The proposed framework outperforms strong models and ChatGPT by 9.8% when evaluated by Exact Match (EM). |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
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| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
| Approach: | They propose to incorporate AMR into generation-based EAE models by generating AMR-aware prefixes for every layer of the generation model. |
| Outcome: | The proposed model generates AMR-aware prefixes for every layer of the generation model and improves the generation. |
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction (2025.findings-emnlp)
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| Challenge: | Existing work evaluates event argument extraction with exact match (EM), where predicted arguments must align exactly with annotated spans. |
| Approach: | They propose a Reliable Evaluation framework for Generative event argument extraction that combines exact, relaxed, and LLM-based matching to better align with human judgment. |
| Outcome: | Experiments on six datasets show that REGen achieves an average performance gain of +23.93 F1 over EM, reflecting capabilities overlooked by prior evaluation. |