Papers with Variational auto-encoders
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. |
Generating Sentences from Disentangled Syntactic and Semantic Spaces (P19-1)
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| Challenge: | Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. |
| Approach: | They propose to generate sentences from disentangled syntactic and semantic spaces by using the linearized tree sequence. |
| Outcome: | The proposed method achieves similar or better performance in various tasks compared with state-of-the-art models. |