Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection (P18-1)
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| Challenge: | Recent studies show that neural networks can be used for event detection but can be contaminated by spurious features. |
| Approach: | They propose a self-regulated learning approach by utilizing a generative adversarial network to generate spurious features. |
| Outcome: | The proposed method is highly effective and adaptable on the ACE 2005 and TAC-KBP 2015 corpora. |
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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. |
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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 . |
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Open-Domain Event Detection using Distant Supervision (C18-1)
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Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
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SELFEXPLAIN: A Self-Explaining Architecture for Neural Text Classifiers (2021.emnlp-main)
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| Challenge: | Existing models that explain text classification predictions are opaque and overfit to spurious artifacts. |
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