Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
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AEDA: An Easier Data Augmentation Technique for Text Classification (2021.findings-emnlp)
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| Challenge: | AEDA is an easier data augmentation technique than EDA. |
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Do sequence-to-sequence VAEs learn global features of sentences? (2020.emnlp-main)
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Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text Generation (2020.findings-emnlp)
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| Challenge: | a new architecture for controlling, generating and augmenting text is being developed for supervised NLP tasks. |
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| Approach: | They propose to integrate CVAE into a span-based Named Entity Recognition model. |
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
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GASE: Generatively Augmented Sentence Encoding (2025.findings-emnlp)
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| Challenge: | Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarizing, or extracting keywords, followed by pooling the original and synthetic embeddings. |
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Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
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| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
| Approach: | They propose to use random resampling, word-level transformations and neural text generation to augment existing data to cope with imbalanced data. |
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EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (D19-1)
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| Challenge: | Existing data augmentation techniques for text classification are difficult to implement and cost a high amount of money. |
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