BatchMixup: Improving Training by Interpolating Hidden States of the Entire Mini-batch (2021.findings-acl)
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| Challenge: | a data augmentation technique is used to augment data, but it has two drawbacks. |
| Approach: | They propose a new mixup paradigm that generates new points scattered throughout the whole mini-batch. |
| Outcome: | The proposed model improves the performance of NLP tasks while using different ratios of training data. |
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| Challenge: | Using MixUp, additional samples are generated during training by combining random pairs of training samples and their labels. |
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| Challenge: | Existing methods to improve the robustness of text classification models are token-, sentence-, and hiddenlevel augmentation. |
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Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models (N18-2)
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SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)
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| Challenge: | Existing methods to handle label noise in text classification tasks are limited to visual data. |
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DMix: Adaptive Distance-aware Interpolative Mixup (2022.acl-short)
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| Challenge: | Interpolation-based regularisation methods such as Mixup have shown to be effective for various tasks and modalities. |
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BERTTune: Fine-Tuning Neural Machine Translation with BERTScore (2021.acl-short)
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| Challenge: | Neural machine translation models are biased toward limited translation references . BERTScore is a scoring function based on contextual embeddings that overcomes the limitations of n-gram-based metrics. |
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Effective Batching for Recurrent Neural Network Grammars (2021.findings-acl)
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| Challenge: | RNNGs are hard to scale due to the difficulty of batched training. |
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