CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
Copied to clipboard
| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
Similar Papers
CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing EE methods do not model event characteristics from large unsupervised data. |
| Approach: | They propose a contrastive pre-training framework for event extraction to better learn event knowledge from large unsupervised data and their semantic structures. |
| Outcome: | The proposed framework improves on ACE 2005 and MAVEN datasets on event extraction tasks. |
LA-UCL: LLM-Augmented Unsupervised Contrastive Learning Framework for Few-Shot Text Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Experimental results show that our model exceeds the baseline models due to the lack of cognitive ability. |
| Approach: | They propose a LLM-Augmented Unsupervised Contrastive Learning Framework which introduces a cognition-enabled Large Language Model (LLM) for efficient data augmentation and presents corresponding contrastive learning strategies. |
| Outcome: | The proposed model exceeds baseline models on six datasets. |
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding (2022.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to learning data representations using contrastive learning perform data augmentation and contrastive training separately. |
| Approach: | They propose a framework that performs data augmentation and contrastive learning end-to-end . they propose to combine data augmented with text encoders to optimize for contrastive training . |
| Outcome: | Experiments on GLUE and Gururangan datasets show the proposed framework is effective in NLP. |
LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
Copied to clipboard
| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
| Approach: | They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system. |
| Outcome: | The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance. |
Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience (2025.findings-acl)
Copied to clipboard
Jiawei Gu, Ziting Xian, Yuanzhen Xie, Ye Liu, Enjie Liu, Ruichao Zhong, Mochi Gao, Yunzhi Tan, Bo Hu, Zang Li
| Challenge: | Large language models struggle to infer implicit relationships embedded in tabular formats . authors introduce a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning (ICL). |
| Approach: | They propose a framework that builds experience memory representations and enhances generalization through contrastive In-Context Learning to simulate human-like knowledge transfer. |
| Outcome: | Experiments on Text-to-SQL and TableQA show CoRE significantly improves performance . it achieves gains of 3.44% and 4.24%, with up to 17.2% on challenging tasks . |
Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
Copied to clipboard
| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
KFCNet: Knowledge Filtering and Contrastive Learning for Generative Commonsense Reasoning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have led to substantial gains over a broad range of NLP tasks, but have limitations for high-quality tasks such as commonsense generation and ad keyword generation. |
| Approach: | They propose a Knowledge Filtering and Contrastive learning Network which references external knowledge and achieves better generation performance. |
| Outcome: | The proposed model outperforms the current state of the art on the CommonGen benchmark by a large margin. |
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)
Copied to clipboard
| 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. |
Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)
Copied to clipboard
Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin
| Challenge: | Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art. |
| Approach: | They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data. |
| Outcome: | The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |