Papers with ED datasets
EDM3: Event Detection as Multi-task Text Generation (2024.starsem-1)
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| Challenge: | Existing methods for Event Detection (ED) cannot easily leverage pre-trained semantic knowledge. |
| Approach: | They propose to decompose and reformulate ED and fine-tune over its atomic subtasks to enhance knowledge transfer while mitigating prediction error propagation inherent in pipelined approaches. |
| Outcome: | The proposed method achieves state-of-the-art performance on RAMS, MAVEN, and MLEE, while achieving 90% accuracy over rare event types. |
MINION: a Large-Scale and Diverse Dataset for Multilingual Event Detection (2022.naacl-main)
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| Challenge: | Existing methods for ED in IE and NLP focus on feature-based models to feature-driven models. |
| Approach: | They propose to use a multilingual dataset to annotate events for 8 different languages . they demonstrate the challenges and transferability of ED across languages in MINION . |
| Outcome: | a new dataset that consistently annotates events for 8 different languages is released . the new dataset will promote future research on multilingual ED . |
Improving Entity Disambiguation by Reasoning over a Knowledge Base (2022.naacl-main)
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| Challenge: | Recent work in entity disambiguation relies on a limited subset of KB facts to link entities . less common entities are prone to missing or inconsistent KB information, which is problematic for models which rely on 'one source' |
| Approach: | They propose an ED model which links entities by reasoning over a symbolic knowledge base in a fully differentiable fashion. |
| Outcome: | The proposed model outperforms state-of-the-art models on six well-established datasets by 1.3 F1 on average. |
Global Entity Disambiguation with BERT (2022.naacl-main)
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| Challenge: | Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base. |
| Approach: | They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia . |
| Outcome: | The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |
Event Detection: Gate Diversity and Syntactic Importance Scores for Graph Convolution Neural Networks (2020.emnlp-main)
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| Challenge: | Recent studies on event detection (ED) have shown that the syntactic dependency graph can be employed in graph convolutional neural networks (GCNs) but the computation of the hidden vectors in such graph-based models is agnostic to the trigger candidate words, leaving irrelevant information for the trigger candidates. |
| Approach: | They propose a mechanism to filter noisy information in the hidden vectors of graph-based models based on the information from the trigger candidate. |
| Outcome: | The proposed model achieves state-of-the-art on two ED datasets. |
Unleash GPT-2 Power for Event Detection (2021.acl-long)
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| Challenge: | Event Detection (ED) aims to recognize mentions of events and their types in text. |
| Approach: | They propose to exploit a pre-trained language model to generate training samples for ED. |
| Outcome: | The proposed model improves on multiple ED benchmark datasets and establishes state-of-the-art results. |
Efficient Overshadowed Entity Disambiguation by Mitigating Shortcut Learning (2024.emnlp-main)
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Panuthep Tasawong, Peerat Limkonchotiwat, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Entity disambiguation (ED) is crucial in natural language processing tasks such as question-answering and information extraction. |
| Approach: | They propose a method to reduce computational overhead on overshadowed entities by addressing shortcut learning. |
| Outcome: | The proposed method achieves state-of-the-art performance without compromising inference speed. |
SNaRe: Domain-aware Data Generation for Low-Resource Event Detection (2025.emnlp-main)
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| Challenge: | Existing methods for ED struggle with label noise and domain drift when applied to specialized domains. |
| Approach: | They propose a domain-aware synthetic data generation framework composed of three components: Scout, Narrator, and Refiner. |
| Outcome: | The proposed framework outperforms baseline approaches on three diverse domain ED datasets and achieves average F1 gains of 3-7% in the zero-shot/few-shot settings and 4-20% improvement for multilingual generation. |