Explicit Role Interaction Network for Event Argument Extraction (2022.findings-emnlp)
Copied to clipboard
| 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. |
Similar Papers
Intra-Event and Inter-Event Dependency-Aware Graph Network for Event Argument Extraction (2023.findings-emnlp)
Copied to clipboard
| 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. |
HMEAE: Hierarchical Modular Event Argument Extraction (D19-1)
Copied to clipboard
| Challenge: | Existing event extraction methods classify each argument role independently, ignoring conceptual correlations between different argument roles. |
| Approach: | They propose a Hierarchical Modular Event Argument Extraction model to provide inductive bias from the concept hierarchy of event argument roles. |
| Outcome: | The proposed model outperforms existing methods on real-world datasets and shows that it leverages useful knowledge from the concept hierarchy. |
Event Pattern-Instance Graph: A Multi-Round Role Representation Learning Strategy for Document-Level Event Argument Extraction (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing role-based span selection strategies ignore interrelations between events . authors propose a multi-round role representation learning strategy for document-level event argument extraction . |
| Approach: | They propose a pattern-instance graph to capture role semantics embedded in various associations . they also propose re-inventing the role representations learned from previous analyzed documents . |
| Outcome: | The proposed model captures role semantics embedded in various associations . iteratively updates representations of role nodes and edges to enrich their semantic information . the model improves prediction performance in subsequent rounds of span selection . |
Open-Vocabulary Argument Role Prediction For Event Extraction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on event extraction depend on pre-defined argument roles . despite great progress, many studies still rely on hand-crafted ontologies . |
| Approach: | They propose an unsupervised framework for customizing argument roles for event extraction . they propose a human-annotated event extraction dataset with 143 customized argument roles . |
| Outcome: | The proposed framework outperforms existing methods on an event extraction dataset. |
Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing work on event-specific argument extraction is limited to contiguous spans of text . Existing approaches to event-centric information extraction are limited to explicit arguments . |
| Approach: | They propose two key argument types that cannot be modeled by existing EE frameworks . implicit and scattered arguments are crucial to elicit full breadth of information required for proper event modeling. |
| Outcome: | The proposed dataset includes 7,464 argument annotations from online health discourse. |
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 . |
Joint Event Extraction with Hierarchical Policy Network (2020.coling-main)
Copied to clipboard
| Challenge: | Existing work on event extraction (EE) is pipelined or uses a joint structure but does not utilize information interactions among event triggers, event arguments, and argument roles. |
| Approach: | They propose to exploit role information of arguments in an event and devise a Hierarchical Policy Network to perform joint EE. |
| Outcome: | The proposed system outperforms existing methods and is more powerful for sentences with multiple events. |
Extracting Trigger-sharing Events via an Event Matrix (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to extract multiple events with triggers and arguments are invalid as there may be multiple events. |
| Approach: | They propose a framework for event extraction which models the relations between arguments by an event matrix. |
| Outcome: | The proposed framework beats all the advanced competitors on 3 widely-used datasets. |
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger. |
| Approach: | They propose a framework to extract event-related arguments from a given event frame-level scope. |
| Outcome: | The proposed method achieves state-of-the-art on the RAMS dataset. |
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. |