Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
Copied to clipboard
| Challenge: | Existing methods to extract event data are laborious to create and limited in size. |
| Approach: | They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. |
| Outcome: | The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods. |
Similar Papers
DEGREE: A Data-Efficient Generation-Based Event Extraction Model (2022.naacl-main)
Copied to clipboard
I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing models for event extraction require expensive human annotations. |
| Approach: | They propose a data-efficient event extraction model that formulates event extraction as a conditional generation problem. |
| Outcome: | The proposed model can be trained with only a few labeled examples. |
Rapid Customization for Event Extraction (P19-3)
Copied to clipboard
| Challenge: | a novel system allows users to customize event extraction to find new event types and their arguments. |
| Approach: | They propose a system that allows a user to find, expand and filter event triggers by exploring an unannotated development corpus. |
| Outcome: | The proposed system can find, expand and filter event triggers from an unannotated development corpus . it trains a generic argument attachment model for extracting Actor, Place, and Time . |
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
| Outcome: | The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios. |
Event Extraction as Question Generation and Answering (2023.acl-short)
Copied to clipboard
| Challenge: | Recent work on Event Extraction addresses the error propagation issue found in token-based classification approaches. |
| Approach: | They propose a Question Generation (QG) model that generates questions that leverage contextual information instead of fixed templates. |
| Outcome: | The proposed model outperforms all previous single-task-based models on the ACE05 English dataset. |
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to event extraction are limited to a set of pre-defined types. |
| Approach: | They propose a natural language query framework that uses event types and argument roles to extract candidate triggers and arguments from input text. |
| Outcome: | The proposed framework outperforms existing methods on zero-shot event extraction. |
Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Event Argument Extraction is a critical subtask of Event Extraction, focused on identifying event arguments within text. |
| Approach: | They propose a Fusion Selection-Generation-Based Approach that merges selective and generative methods to enhance argument extraction accuracy. |
| Outcome: | The proposed method improves on the RAMS and WikiEvents, while preserving the unique characteristics of both methods. |
Few-Shot Event Argument Extraction Based on a Meta-Learning Approach (2024.naacl-srw)
Copied to clipboard
| Challenge: | Recent studies on few-shot event extraction focus on event trigger detection and argument extraction in meta-learning contexts. |
| Approach: | They propose to use prototypical networks to perform few-shot event argument extraction . they propose to inject syntactic knowledge into the model to enhance relation embeddings . |
| Outcome: | The proposed approach achieves strong performance on ACE 2005 in several few-shot configurations. |
Demonstration Retrieval-Augmented Generative Event Argument Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
| Approach: | They propose a generative EAE that uses event knowledge-injected generator and demonstration retriever to generate event arguments from training data. |
| Outcome: | The proposed method outperforms baselines and can be generalized to various datasets. |
Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
| Approach: | They propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. |
| Outcome: | The proposed model achieves 7.6% F1 and 5.7% F1 over the best baseline on the document-level event extraction dataset WikiEvents and 9.3% F1 on the informative argument extraction task. |
Zero-shot Label-Aware Event Trigger and Argument Classification (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing work on event extraction relies on labor-intensive annotation, ignoring semantic meaning of event types' labels. |
| Approach: | They propose a zero-shot event extraction approach that first identifies events with existing tools and then maps them to a given taxonomy of event types in a no-shot manner. |
| Outcome: | The proposed approach doubles the performance of previous approaches on a ACE-2005 dataset . it leverages label representations induced by pre-trained language models and maps events to the target types . |