Papers by David Lowell
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data (2021.emnlp-main)
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| Challenge: | Unsupervised Data Augmentation (UDA) is a semisupervised learning method that penalizes differences between a model's predictions on unlabeled examples and corresponding 'noised' examples produced via data augmentation. |
| Approach: | They propose to use a consistency loss to penalize differences between models' predictions on unlabeled and unlabed examples to enforce consistency between models and their perturbed counterparts. |
| Outcome: | The proposed method is able to penalize differences between models' outputs on unlabeled and unlabed examples without complex data augmentation. |
Practical Obstacles to Deploying Active Learning (D19-1)
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| Challenge: | Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. |
| Approach: | They propose to use active learning to optimize predictive performance . they find that current approaches do not generalize reliably across models and tasks . |
| Outcome: | The proposed approach outperforms training on i.i.d. datasets on supervised learning tasks. |