| 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. |
Similar Papers
Adversarial Training for Weakly Supervised Event Detection (N19-1)
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| 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. |
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. |
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. |
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 . |
Introducing a New Dataset for Event Detection in Cybersecurity Texts (2020.emnlp-main)
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| Challenge: | a large amount of text data is produced to report and discuss cyber vulnerabilities . detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. |
| Approach: | They propose a dataset characterizing the manual annotation for 30 important cybersecurity event types and a large dataset to develop deep learning models. |
| Outcome: | The proposed dataset characterizes the manual annotation for 30 important event types and supports the modeling of document-level information to improve the performance. |
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (2021.findings-acl)
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| Challenge: | Event detection typically does not have sufficient labelled data, thus can be formulated as a few-shot learning problem. |
| Approach: | They propose a knowledge-based fewshot event detection method which introduces external event knowledge as the knowledge prior of event types. |
| Outcome: | Experiments show that the proposed method outperforms baselines by 15 F 1 points . event detection is an important task in information extraction . |
Saliency as Evidence: Event Detection with Trigger Saliency Attribution (2022.acl-long)
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| Challenge: | Existing methods to ED see no differences between event types and use a single model to address them all. |
| Approach: | They propose a new concept termed trigger salience attribution which can explicitly quantify the underlying patterns of events. |
| Outcome: | The proposed model can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks. |
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
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| 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. |