Papers with mixup strategy
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. |
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency (2022.acl-long)
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| Challenge: | Existing studies have shown that mixing up can improve model calibration on image classification tasks, but little is known about using it on natural language understanding (NLU) tasks. |
| Approach: | They propose a mixup strategy for pre-trained language models that improves model calibration further by using the AUM statistic and saliency map. |
| Outcome: | The proposed mixup improves model calibration on natural language understanding tasks while maintaining competitive accuracy. |
Calibrating Student Models for Emotion-related Tasks (2022.emnlp-main)
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| Challenge: | Knowledge distillation is an effective method to transfer knowledge from one network (a.k.a. teacher) to another (as student). |
| Approach: | They propose to use a mixup data augmentation technique to increase the accuracy of the model by providing better training signals to the student models. |
| Outcome: | The proposed method improves the calibration of student models while providing better training signals to the student models using training dynamics. |