Papers with pseudo training
Manifold Adversarial Augmentation for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Recent studies show that NMT models can drop significantly when small perturbations are added to input sentences. |
| Approach: | They propose a data augmentation approach to sample sentences from the vicinity distributions in higher-level representations. |
| Outcome: | The proposed method improves translation accuracy on training samples from higher-level representations. |
Learning from a Friend: Improving Event Extraction via Self-Training with Feedback from Abstract Meaning Representation (2023.findings-acl)
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| Challenge: | Existing data scarcity hinders the progress of event extraction, authors say . ACE-052 has 10 of the 33 event types with less than 80 annotations, authors claim . |
| Approach: | They propose a self-training with feedback framework that leverages large-scale unlabeled data to acquire feedback for each new event prediction from the unlabed data. |
| Outcome: | The proposed framework improves event extraction models even when unlabeled data are unavailable. |