MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (2020.acl-main)
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| Challenge: | Xie et al., 2016) demonstrate that semi-supervised learning models suffer from over-fitting when there is only limited labeled data. |
| Approach: | They propose a semi-supervised learning method for text classification using a data augmentation method called TMix. |
| Outcome: | The proposed method outperforms pre-trained and fine-tuned models on several text classification benchmarks. |
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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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| Challenge: | Existing approaches to semi-supervised text classification suffer from pseudo-label bias and error accumulation. |
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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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