Contextualized Soft Prompts for Extraction of Event Arguments (2023.findings-acl)
Copied to clipboard
| 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. |
Similar Papers
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. |
Bi-Directional Iterative Prompt-Tuning for Event Argument Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing prompt-tuning methods for event argument extraction lack entity information . eAE is a key step of event extraction, but it requires a pre-trained language model to extract event arguments. |
| Approach: | They propose a prompt-tuning method that takes advantage of entity information and pre-trained language models. |
| Outcome: | The proposed method outperforms the state-of-the-art prompt-tuning methods on an english dataset. |
Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2022.acl-long)
Copied to clipboard
| Challenge: | Using a prompt-based model, we find that event argument extraction is efficient and generalized well to few-shot settings. |
| Approach: | They propose a model PAIE for event argument extraction using prompt tuning for extractive objectives. |
| Outcome: | The proposed model can extract arguments with the same role instead of heuristic threshold tuning. |
TISE: A Tripartite In-context Selection Method for Event Argument Extraction (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent studies show that LLMs can finish inference by providing several examples. |
| Approach: | They propose a method which integrates three requirements when selecting an in-context example and integrates them into a set of determinantal point processes to enhance the reasoning capabilities of LLMs. |
| Outcome: | The proposed method can achieve superior performance with fewer examples and outperform some supervised methods. |
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction (2024.findings-acl)
Copied to clipboard
Wanlong Liu, Li Zhou, DingYi Zeng, Yichen Xiao, Shaohuan Cheng, Chen Zhang, Grandee Lee, Malu Zhang, Wenyu Chen
| Challenge: | mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring correlations among multiple events. |
| Approach: | They propose a multi-event argument argument extraction model which extracts arguments from all events simultaneously. |
| Outcome: | The proposed model performs better on four public datasets while saving time. |
From Simple to Complex: A Progressive Framework for Document-level Informative Argument Extraction (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for document-level event argument extraction use memory to store the results of already predicted events. |
| Approach: | They propose a simple-to-complex progressive framework for document-level event argument extraction . they first calculate the difficulty of each event and then conduct the extraction following a simpler order . |
| Outcome: | The proposed model outperforms previous methods by 1.4% in the document-level EAE task. |
Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences? (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies on event argument extraction (EAE) have not taken event co-occurrences into account. |
| Approach: | They propose to reformulate event co-occurrences as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework that extracts the arguments of multiple events in parallel. |
| Outcome: | The proposed framework can extract arguments of multiple events in parallel. |
Few-Shot Document-Level Event Argument Extraction (2023.acl-long)
Copied to clipboard
| 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 . |
Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for incorporating entities into EAE rely on prompts or NER . weak semantic associations due to missing role-entity correspondence cues . one-sided semantic understanding relying solely on argument role semantics a problem . |
| Approach: | They propose an EAE model with stage-customized entity type embedding to explore the role of entity types. |
| Outcome: | The proposed model achieves state-of-the-art performance on mainstream benchmarks and robustness in low-resource settings. |
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. |