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. |
Similar Papers
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
| Approach: | They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions . |
| Outcome: | The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation . |
Retrieval-Augmented Generative Question Answering for Event Argument Extraction (2022.emnlp-main)
Copied to clipboard
| 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. |
AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (2023.acl-long)
Copied to clipboard
| 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. |
Resource-Enhanced Neural Model for Event Argument Extraction (2020.findings-emnlp)
Copied to clipboard
| 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. |
REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction (2025.findings-emnlp)
Copied to clipboard
| 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. |
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. |
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. |
Document-Level Event-Argument Data Augmentation for Challenging Role Types (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for Event Argument Extraction (EAE) are not well-suited to a variety of real-world situations, including long documents and challenging role types. |
| Approach: | They propose two novel methods for generating document-level EAE samples using zero in-domain training data and validate their generalizability. |
| Outcome: | The proposed methods show significant performance increases in low-resource settings. |
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. |
ArgGen: Prompting Text Generation Models for Document-Level Event-Argument Aggregation (2022.findings-aacl)
Copied to clipboard
| Challenge: | Existing discourse-level information extraction tasks are extractive in nature, but extracting information from larger bodies of discourse-like documents requires more natural language understanding and reasoning capabilities. |
| Approach: | They propose a conditional text generation approach which generates consolidated event-arguments at a document-level with minimal loss of information. |
| Outcome: | The proposed approach generates document-level argument spans in a low-resource and zero-shot setting and can be leveraged in other related multilingual text generation tasks. |