| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art methods on two real-world datasets. |
Similar Papers
Learning with Partial Annotations for Event Detection (2023.acl-long)
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| Challenge: | Event detection (ED) requires fully labeled and high-quality training data. |
| Approach: | They propose a new trigger localization formulation using contrastive learning to distinguish ground-truth triggers from contexts and show a decent robustness for addressing partial annotation noise. |
| Outcome: | The proposed approach achieves an F1 score of over 60% in an extreme scenario where 90% of events are unlabeled. |
How Does Context Matter? On the Robustness of Event Detection with Context-Selective Mask Generalization (2020.findings-emnlp)
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| Challenge: | Existing studies focus on improving the overall performance of an ED model, but few consider the robustness of an existing model. |
| Approach: | They propose a new training mechanism that can effectively mine context-specific patterns for learning and robustify an ED model. |
| Outcome: | The proposed model can learn a complementary predictive bias with most ED models that use full context for feature learning. |
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
| Approach: | They propose to use Wikipedia sections to extract weak labels for sentences describing economic events from text. |
| Outcome: | The proposed method can extract weak labels for sentences describing economic events from Wikipedia sentences. |
Event Detection without Triggers (N19-1)
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| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |
Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 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. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
| Approach: | They present a MAssive eVENt detection dataset which contains 4,480 Wikipedia documents and 168 event types. |
| Outcome: | The proposed dataset shows that existing methods cannot achieve promising results on the small datasets. |
Open-Domain Event Detection using Distant Supervision (C18-1)
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| Challenge: | Existing work on restricted domains and event annotation has limited coverage of events. |
| Approach: | They propose a distant supervision method that generates high-quality training data . they use a manually annotated corpus as a model to investigate events in various domains . |
| Outcome: | The proposed method outperforms supervised models in a manually annotated event corpus despite no direct supervision . |
Event-Related Bias Removal for Real-time Disaster Events (2020.findings-emnlp)
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| Challenge: | Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. |
| Approach: | They propose to train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification. |
| Outcome: | The proposed model removes event-specific biases and improves on tweet importance classification. |